JP7254846B2 - Diagnostic device, diagnostic method, and diagnostic program - Google Patents

Description

The present invention relates to a diagnostic device, diagnostic method, and diagnostic program.

In recent years, techniques for diagnosing aging of various devices have become widespread (see Patent Document 1, for example).

Japanese Patent No. 6801131

As an example of a method for diagnosing an apparatus, for example, it is possible to compare changes in vibration levels before and after maintenance work of equipment, and to confirm that the maintenance work has been properly performed. In such a diagnostic method, for example, vibration data of a temporary or permanent vibration sensor before and after maintenance work is started is collected and analyzed. However, in general, the operator simply records the indicated values of the sensors visually confirmed and confirms whether there are significant fluctuations before and after the maintenance work, or whether the indicated values are within the reference values. It is not possible to easily grasp changes that cannot be grasped without detailed analysis of the data. In particular, in equipment where there are multiple processes in one operation cycle, the indicated values often differ between the processes, and it is common that the indicated values in the next process differ according to the state in the previous process.

Therefore, it is an object of the present invention to enable easy comparison of time-series data indicating the state of equipment for each operation cycle even for equipment in which a plurality of processes exist in one operation cycle.

In order to solve the above problems, in the present invention, group numbers assigned in order of magnitude of a representative value indicating the relative relationship of data in each unit period to data in all periods in a specific operation cycle are assigned to data in each unit period. , and the group numbers plotted in chronological order for each unit period are output on the screen of a graph that is superimposed for a plurality of operation cycles.

Specifically, the present invention is a diagnostic device for diagnosing the state of a facility having a plurality of processes in one operation cycle, comprising: a storage unit for storing time-series data of a plurality of sensors indicating the state of the facility; and a processing unit that determines a state change of the equipment from the time-series data, and the processing unit divides data from the start to the end of a specific operation cycle out of the time-series data into unit periods, and specifies A calculation process that calculates for each unit period the representative value that indicates the relative relationship of the data for each unit period with respect to the data for the entire period in the operation cycle, and the group number assigned in order of the magnitude of the representative value for each unit period. Grouping process to be assigned to data, and output process to output a screen of a graph showing the group numbers assigned to the data of each unit period, plotted in chronological order of each unit period, overlaid for multiple operation cycles. and run

Here, the group number is a group name given to the data of each unit period, and is not limited to numbers, but numbers that can be easily set on the vertical axis of the graph, for example, are preferable.

With the above diagnostic device, data from the start to the end of a specific operation cycle and data from the start to the end of another operation cycle for equipment that has multiple processes in one operation cycle It is possible to easily grasp the relationship for each unit period relative to . Therefore, even if the facility has a plurality of processes in one operation cycle, it is possible to easily grasp the state change of the facility.

In the calculation process, the processing unit squares the standardized value representing the degree of variation in data for each unit period, and calculates for each unit period the value obtained by accumulating the values for a plurality of sensors as a representative value. may In this case, the processing unit divides the data of each unit period into a predetermined number and calculates the difference between the maximum value and the minimum value individually calculated. A value μ and a standard deviation σ may be calculated, and a standardized value may be calculated using the average value Δ, the average value μ and the standard deviation σ.

According to this, it is possible to calculate a representative value representing the relative relationship of the data of each unit period with respect to the data of the entire period in a specific driving cycle. The standardized value is a value that indicates the degree of variation with respect to a reference value. can do.

In addition, in the grouping process, the processing unit determines the range of the minimum value and the maximum value of the difference in the magnitude of the data variation in a specific unit period among the unit periods, and the range of the difference in the magnitude of the data variation in the other specific unit period. The representative values in which the ranges of the minimum value and the maximum value of the difference overlap may be regarded as having the same level, and the same group number may be given to each data of both unit periods. According to this, it is possible to group the data into the number of groups that does not hinder practical use.

Also, the sensor may be a vibration sensor, and the time-series data may be data on the vibration level of the facility. Vibrations emitted by operating equipment are usually continuous. Therefore, if the above diagnosis device is used with the data of the vibration sensor, it is suitable for grasping the state change of the equipment.

The present invention can also be viewed from a method aspect. The present invention is, for example, a diagnostic method for diagnosing the state of equipment having a plurality of processes in one operation cycle, in which a computer detects a specific operation cycle out of time-series data of a plurality of sensors indicating the state of the equipment. A calculation process that divides the data from the start to the end of each unit period into units, and calculates for each unit period a representative value that indicates the relative relationship of the data for each unit period to the data for the entire period in a specific operation cycle. A grouping process in which a group number assigned in order of representative value is assigned to the data of each unit period, and a plot of the group numbers assigned to the data of each unit period in chronological order of each unit period. , and an output process of outputting a screen of graphs superimposed for a plurality of operation cycles.

Moreover, the present invention can also be understood from the aspect of a program. The present invention is, for example, a diagnostic program for diagnosing the state of a facility in which a plurality of processes exist in one operation cycle, wherein a computer is provided with time-series data of a plurality of sensors indicating the state of the facility. A calculation process that divides the data from the start to the end of each unit period into units, and calculates for each unit period a representative value that indicates the relative relationship of the data for each unit period to the data for the entire period in a specific operation cycle. A grouping process in which a group number assigned in order of representative value is assigned to the data of each unit period, and a plot of the group numbers assigned to the data of each unit period in chronological order of each unit period. , and an output process of outputting a screen of graphs superimposed for a plurality of operation cycles.

With the diagnostic device, diagnostic method, and diagnostic program described above, it is possible to easily compare the time-series data indicating the state of the facility for each operation cycle even if the facility has multiple processes in one operation cycle. Become.

FIG. 1 is a diagram showing an example of a system configuration of a diagnostic system according to an embodiment. FIG. 2 is a diagram showing an example of a processing flow implemented by a computer. FIG. 3 is a diagram explaining the content of vibration data processing in a specific driving cycle. FIG. 4 is a diagram explaining sorting of representative values and assignment of group numbers. FIG. 5 is a diagram illustrating the correlation between the magnitude of the vibration level detected by each sensor and the frequency of occurrence. FIG. 6 is a diagram explaining the idea of the correlation between the vibration levels of the sensors. FIG. 7 is an image of group number sorting and graph drawing processing in a specific driving cycle. FIG. 8 is a diagram showing an image of the mixing process. FIG. 9 is a graph showing a graph of the operation cycle to be compared so as to be able to be compared with the base pattern.

Embodiments will be described below. The embodiments shown below are merely examples, and do not limit the technical scope of the present disclosure to the following aspects.

<Hardware configuration>
Drawing 1 is a figure showing an example of the system configuration of diagnostic system 1 concerning an embodiment. A diagnostic system 1 is a system for diagnosing equipment 5 installed in a facility 4 . The diagnostic system 1 is a system for equipment 5 in which a plurality of processes exist in one operation cycle, and the vibration generated by the equipment 5 from the start to the end of one operation cycle composed of a plurality of processes. level can be compared between each driving cycle. The diagnostic system 1 can be applied, for example, to comparison before and after periodic maintenance work of the equipment 5, comparison between trial operation and actual operation, and relative state changes in vibration at various other timings. Therefore, the diagnostic system 1 obtains data on the vibration of the facility 5 with the temporary or permanent vibration sensor 6 attached to the facility 5 , analyzes the data, and diagnoses the facility 5 . The equipment 5 of the facility 4 that is diagnosed by the diagnosis system 1 includes various equipment that can generate vibration during operation. Examples of such equipment 5 include production equipment for pharmaceuticals and industrial products, power generation equipment, transport equipment, and various other equipment.

A vibration sensor 6 that detects vibrations of the equipment 5 transmits data by wire or wirelessly. Data transmitted from the vibration sensor 6 is uploaded to the cloud 3 via a computer or the like installed in the facility 4 . The computer 2 (which is an example of the “diagnostic device” referred to in the present application) analyzes the data uploaded to the cloud 3 and performs abnormality detection and the like of the equipment 5 . The computer 2 is an electronic computer having a CPU 21, a memory 22, a storage 23, and a communication interface 24. By executing a computer program read from the storage 23 and developed in the memory 22, various processes described later are executed. do. The computer 2 may be installed at a location remote from the facility 4 or may be installed at the facility 4 .

The computer 2 implements the following processes by executing the computer program. FIG. 2 is a diagram showing an example of a processing flow implemented by the computer 2. As shown in FIG. When the computer 2 executes the computer program, it implements a series of processing flows from step S101 to step S112 shown in FIG. The processing flow realized by the computer 2 will be described below.

The computer 2 first acquires the vibration data uploaded to the cloud 3 (S101). That is, the computer 2 stores vibration data measured by the vibration sensor 6 in the memory 22 . Vibration data is physical quantity data related to vibration, and includes, for example, various vibration levels such as magnitude of amplitude. The vibration data stored in memory 22 may be real-time data that is accumulated sequentially during operation of computer 2 . Vibration data may be sent directly from the vibration sensor 6 to the computer 2 instead of being uploaded to the cloud 3 .

Next, the computer 2 extracts data from the start to the end of a specific reference driving cycle from the vibration data, and divides the extracted vibration data into a predetermined number (S102). FIG. 3 is a diagram explaining the content of vibration data processing in a specific driving cycle. There are various periods of data to be extracted from the vibration data. It is preferable to extract the vibration data from the start to the end of the cycle and the vibration data from the start to the end of the first operation cycle executed after the completion of the maintenance work as the vibration data of the reference operation cycle. . The number of divisions can be appropriately determined according to the computing power and diagnostic accuracy of the computer 2. Here, the case where the vibration data in a specific driving cycle is divided into n vibration data T1 to Tn is taken as an example. to explain. A period on the time axis corresponding to each vibration data T1 to Tn is hereinafter referred to as a "unit period".

Furthermore, the computer 2 further divides each of the vibration data T1 to Tn into a predetermined number. The number of divisions can be appropriately determined according to the computing power and diagnostic accuracy of the computer 2, as in the above case. explain.

Next, the computer 2 calculates a difference by subtracting the minimum value from the maximum value for each of the five divided vibration data S1 to S5 (S103). Differences between the five vibration data S1 to S5 are hereinafter referred to as differences Δ1 to Δ5. The computer 2 calculates the difference between the divided vibration data, for example, for all the sensor data. A general vibration sensor normally outputs vibration data for each of the XYZ axes. Therefore, in the case where the vibration sensor 6 outputs respective vibrations of three axes, the computer 2 calculates the difference between the divided vibration data for each of the three vibration data corresponding to one vibration sensor 6. is calculated. The vibration sensor 6 thus outputs three types of vibration data. For convenience of explanation, the term "sensor output" or "vibration It is sometimes called a factor.

Next, the computer 2 calculates the average value Δ of the differences Δ1 to Δ5 (S104). The average value Δ is obtained for all vibration data T1 to Tn. Therefore, hereinafter, when the average value Δ of any of the vibration data T1 to Tn is meant, the corresponding symbols T1 to Tn will be attached and explained (for example, if the average value Δ of the vibration data T1 is “ mean value _ΔT1 ”). The computer 2 calculates the average value Δ based on the following formula.

Next, the average value μ and the standard deviation σ of the vibration data for the entire period in the specific driving cycle from which the data was extracted are calculated (S105). The average value μ and standard deviation σ of the vibration data for the entire period in the specific driving cycle from which the data was extracted are calculated based on the following equations. That is, the average value μ is the average value of all (5×n) differences Δ1 to 5 of the five vibration data S1 to S5 respectively corresponding to the vibration data T1 to Tn.

Next, a standardized value Θ of each vibration data T1 to Tn is calculated (S106). Since the standardized value Θ is calculated for each of the vibration data T1 to Tn, hereinafter, when referring to a specific standardized value Θ, the corresponding symbols T1 to Tn will be used for explanation (for example, the vibration data T1 If it is a standardized value Θ, then “standardized value Θ _T1 ”). The standardized value Θ is calculated by standardizing the average value Δ of each vibration data T1 to Tn using the average value μ and the standard deviation σ of the vibration data over the entire period in the specific driving cycle from which the data was extracted. Therefore, the computer 2 calculates the standardized value Θ based on the following formula.

Next, for each of the vibration data T1 to Tn, a representative value P that quantitatively represents the state of vibration is calculated (S107). The standardized value Θ is both positive and negative. Therefore, the representative value P is obtained by squaring the standardized value Θ and integrating it for all vibration factors (all sensors). Specifically, the representative value P of each unit period is calculated based on the following formula.

The vibration sensor 6 normally outputs three vibration data of the X-axis, the Y-axis, and the Z-axis, as described above. Therefore, in the above formula, when there are three vibration sensors 6, the number of sensors M is 9 (3×3). Also, the period number is a number from 1 to n corresponding to the vibration data T1 to Tn.

Next, the computer 2 sorts (arranges) the n representative values P in ascending order (S108). Then, a group number is assigned in the order of the representative values P (S109). Among the n representative values P, the same group number is assigned to the values having the same level. FIG. 4 is a diagram explaining sorting of representative values P and assignment of group numbers. After calculating the representative value P in step S107 as shown in FIG. 4A, the computer 2 sorts the representative value P in ascending order in step S108 as shown in FIG. 4B. Then, as shown in FIG. 4(C), the computer 2 performs level determination for sorting out the values having the same level among the n representative values P. As shown in FIG. Then, as shown in FIG. 4(D), the computer 2 numbers the same group number for the same level. FIG. 4 shows an example in which three representative values P33, P34, and P21 are determined to have the same level of value and are given the same group number "Gr1". The determination as to whether or not the magnitudes of the representative values P are at the same level is performed based on the following concept.

FIG. 5 is a diagram illustrating the correlation between the magnitude of the vibration level detected by each sensor and the frequency of occurrence. In each graph of FIG. 5, the horizontal axis represents the magnitude of the vibration level, and the vertical axis represents the frequency of occurrence. The graph shown in (A) in FIG. 5 illustrates a case where there is a clear difference between the magnitudes of the vibration levels detected by the two sensors. Graph (B) in FIG. 5 illustrates a case where the magnitudes of the vibration levels detected by the two sensors are relatively similar. When there is a clear difference between the vibration levels detected by the two sensors, the relative magnitude relationship between the vibration data measured by each sensor can be clearly distinguished as shown in FIG. 5(A). On the other hand, when the magnitudes of the vibration levels detected by the two sensors are relatively similar, as shown in Fig. 5(B), the relative magnitudes of the vibration data measured by each sensor are clearly distinguishable. Sometimes I can't. Therefore, the determination as to whether or not the magnitudes of the representative values P are at the same level is basically performed as follows.

That is, in case 1 shown in FIG. 5A, the correlation between the vibration level of sensor A and the vibration level of sensor B is clearly lower than B (A<B). I can say Therefore, in the present embodiment, sensor A and sensor B are defined as separate groups for case 1 in which the magnitude relationship between vibration levels is clear. In case 2 shown in FIG. 5B, the correlation between the vibration level of sensor A and the vibration level of sensor B is when A is less than or equal to B (A≦B), It can be said that A is greater than or equal to B (A≧B). Therefore, in this embodiment, sensor A and sensor B are defined as being in the same group for case 2 where the magnitude relationship between vibration levels is not clear.

FIG. 6 is a diagram explaining the idea of the correlation between the vibration levels of the sensors. In the graph of FIG. 6, the horizontal axis represents the magnitude of the vibration level, and the vertical axis represents the frequency of occurrence. The frequency of occurrence of the vibration level output by a specific sensor basically follows a normal distribution with the mean value μ as the apex. Vibration levels PH1 and PL1 shown in the graph of FIG. 6 are data that rarely occur (rare case values). Vibration levels PH2 and PL2 shown in the graph of FIG. 6 are abnormal data (outliers). Since the probability that the average value μ exceeds twice the standard deviation σ (μ±2σ) is 4.5%, the probability that the magnitude relationship of the vibration levels of the two sensors to be compared is reversed is about 0.05% (2 .25% x 2.25%), which is a very low probability. Therefore, in this embodiment, if the value of μ+2σ at the vibration level of a specific sensor is smaller than the value of μ−2σ at the vibration level of another sensor, both sensors are treated as separate groups in principle.

It should be noted that how the threshold value is set can be appropriately changed according to the vibration level data. For example, the area of the range of ±σ is 68.3%, the area of the range of ±2σ is 95.5%, and the area of the range of ±3σ is 99.7%, so the number of sensors and the vibration level An appropriate threshold is set according to the degree of variation of the data, etc., as the threshold for grouping.

Based on the above concept, the computer 2 specifically determines whether the values of the representative values P calculated based on the vibration data are at the same level as follows. Execute with processing.

That is, the computer 2 uses the maximum value, minimum value, average value, and standard deviation of each of the vibration data S1 to S5 obtained by dividing each vibration data T1 to Tn into five in step S102 for each vibration factor. , Θρ, which is the standardized minimum value of the variation of each vibration data T1 to Tn, and Θη, which is the standardized maximum value, are calculated. Θρ and Θη are calculated as follows.

First, let η, ρ, Δ, and s be the maximum value, minimum value, average value, and standard deviation of the vibration data S1 to S5, respectively. Then, for each of the vibration data S1 to S5, the minimum value Δρ and the maximum value Δη of the difference in magnitude of variation in the vibration data are determined based on the following equations. In addition, the coefficient of s in the following formula is a value appropriately determined in advance according to the characteristics of the equipment 5 and the like.

Next, for each vibration factor, all the vibration data S1 to S5 in the operation cycle, that is, the average value μ of (5×3×n) Δ and the standard deviation σ are calculated. Then, using the calculated average μ and standard deviation σ, Θρ obtained by standardizing Δρ of each unit period and Θη obtained by standardizing Δη of each unit period are calculated. Specifically, Θρ and Θη are calculated based on the following equations.

Normalized values can be mixed both positive and negative. Therefore, similarly to the calculation of the representative value P, the calculated Θρ and Θη are squared to calculate the minimum representative value Pρ and the maximum representative value Pη integrated for all vibration factors (all sensors). Specifically, the representative values Pρ and Pη for each unit period are calculated based on the following equations.

Then, after sorting the representative values P in ascending order in step S108, the representative values P for which the ranges of the representative values Pρ and Pη corresponding to the respective representative values P overlap with each other are judged to be of the same level. to complete the process of step S109, that is, the process of assigning a group number. The formula showing the method of determining whether or not they are of the same level is shown below. The following formula illustrates a case where "P41<P53<P40<..." is obtained as a result of sorting the representative values P in ascending order. Assuming that the group number of the representative value Pn is Gn, the group number is assigned according to the criteria shown in the following formula.

That is, the above formula shows an example in which "1" is set as the group number G41 of the representative value P41, which is the smallest among the n representative values P. Then, if Pη41 corresponding to the representative value P41 is smaller than the representative value Pρ53 corresponding to the representative value P53, which is the next largest value after the representative value P41, the minimum representative value Pρ53 and the maximum representative value corresponding to the representative value P53 Since the range of the minimum representative value Pρ41 and the maximum representative value Pη41 corresponding to the representative value P41 does not overlap with the range of Pη53, the representative value P53 is considered not to be at the same level as the representative value P41. An example is shown in which "2" is set as the group number G53 of P53. Further, if the maximum representative value Pη41 corresponding to the representative value P41 is equal to or greater than the minimum representative value Pρ53 corresponding to the representative value P53, which is the next largest value after the representative value P41, the minimum representative value Pρ53 corresponding to the representative value P53 Since the range of the minimum representative value Pρ41 and the maximum representative value Pη41 corresponding to the representative value P41 overlaps the range of the value Pρ53 and the maximum representative value Pη53, the representative value P53 is at the same level as the representative value P41. , and "1", which is the same as the group number G41 of the representative value P41, is set as the group number G53 of the representative value P53.

By the above processing, the numbering of the group number is completed for each of the n representative values P.

Next, the computer 2 sorts the numbered group numbers in chronological order of the vibration data corresponding to each group number (S110). Then, the computer 2 creates a graph in which the horizontal axis is time series and the vertical axis is the group number (S111). FIG. 7 is an image of group number sorting and graph drawing processing in a specific driving cycle. When the computer 2 executes the process of step S110, for example, as shown in FIG. 7, the group numbers are sorted in chronological order so as to correspond to each unit period of the vibration data T1 to Tn. Therefore, if the group numbers sorted in chronological order are plotted on a graph whose vertical axis is the group number, as shown in FIG. A graphical representation is completed. Hereinafter, the pattern indicated by the polygonal line of this graph will be referred to as the base pattern.

The computer 2 then compares the base pattern representing the characteristics of the vibration data of the reference operation cycle with the comparison pattern representing the characteristics of the vibration data of the operation cycle to be compared. Specifically, the computer 2 executes the following process to process the vibration data of the operation cycle to be compared (S112).

That is, the computer 2 first calculates the representative value P from the vibration data of the driving cycle to be compared. The calculation method is the same as the processing of steps S102 to S107 described above. Next, the representative value P calculated from the vibration data of the reference driving cycle and the representative value P calculated from the vibration data of the driving cycle to be compared are mixed. FIG. 8 is a diagram showing an image of the mixing process. Next, the computer 2 performs the same processing as in steps S108 and S109 on the mixed representative value P, and assigns a group number. Then, the computer 2 sorts the numbered group numbers into those corresponding to the reference operation cycle and those corresponding to the operation cycle to be compared. Then, the computer 2 performs the same processing as in step S110 for each sorted group number, and for the group number corresponding to the reference driving cycle and the group number corresponding to the driving cycle to be compared, Sort the vibration data corresponding to each group number in chronological order. Then, as in step S111, the computer 2 creates a graph in which the horizontal axis is the time series and the vertical axis is the group number. FIG. 9 is a graph showing a graph of the operation cycle to be compared so as to be able to be compared with the base pattern.

The graph of FIG. 9 shows graphs of four operation cycles for comparison, in addition to a graph of a base pattern showing a specific operation cycle as a reference. In the graph of FIG. 9, the group number indicated by the vertical axis is irrelevant to the magnitude of the vibration level. The greater the deviation from the average value of the difference between the magnitudes of the vibration values, the greater the variation indicated by the graph line. In the example shown in FIG. 9, by comparing the five graph lines, it can be seen that the deviation from the average value of the difference between the magnitudes of the vibration values is large in the initial stage of starting each operation cycle. For example, when the facility 5 has a process in which the inside of the vacuum chamber is evacuated by a vacuum pump in the initial stage of the operation cycle, the opening of the valve on the suction side of the vacuum pump and the sealing of the opening and closing portion of the vacuum chamber The vibration level generated by the vacuum pump changes depending on the degree of vibration. Therefore, in the case where the equipment 5 is such, at the initial stage of starting each operation cycle, as shown in FIG.

The computer 2 of the present embodiment thus outputs a screen of a graph that enables a visual grasp of the deviation from the average value of the difference in magnitude of the vibration values. Therefore, with the diagnostic system 1, even if the equipment 5 has a plurality of processes in one operation cycle, the level of vibration generated by the equipment 5 from the start to the end in one operation cycle can be determined for each operation. It is possible to compare between cycles. Therefore, the manager of the equipment 5 can use the graph as a reference when examining the equipment 5 and determining whether maintenance is necessary or not.

In this embodiment, the case of vibration data has been described as an example, but it is also possible to apply to various measurement data other than vibration.

<Computer-readable recording medium>
A program that causes a computer or other machine or device (hereinafter referred to as a computer or the like) to implement any of the functions described above can be recorded in a computer-readable recording medium. By causing a computer or the like to read and execute the program of this recording medium, the function can be provided.

Here, a computer-readable recording medium is a recording medium that stores information such as data and programs by electrical, magnetic, optical, mechanical, or chemical action and can be read by a computer, etc. Say. Examples of such recording media that can be removed from a computer or the like include flexible discs, magneto-optical discs, CD-ROMs, CD-R/Ws, DVDs, Blu-ray discs (Blu-ray is a registered trademark), DAT, and 8mm tapes. , memory cards such as flash memory. In addition, there are a hard disk, a ROM (read only memory), and the like as a recording medium fixed to a computer or the like.

1... Diagnosis system 2... Computer 3... Cloud 4... Facility 5... Facility 6... Vibration sensor 21... CPU
22 Memory 23 Storage 24 Communication interface

Claims (7)

A diagnostic device for diagnosing the state of equipment in which a plurality of processes exist in one operation cycle,
a storage unit that stores time-series data of a plurality of sensors indicating the state of the equipment;
a processing unit that determines a state change of the equipment from the time-series data,
The processing unit is
The data from the start to the end of a specific operation cycle in the time-series data is divided into unit periods, and the relative relationship of the data of each unit period to the data of the entire period in the specific operation cycle is shown. a calculation process of calculating a representative value for each unit period;
A grouping process of assigning a group number numbered in order of magnitude of the representative value to the data of each unit period;
an output process of outputting a screen of a graph showing the group numbers assigned to the data of each unit period, plotted in chronological order of each unit period, overlaid for a plurality of operation cycles;
diagnostic equipment. In the calculation process, the processing unit squares a standardized value representing the degree of variation in data in each unit period, and calculates for each unit period the value obtained by integrating the values for the plurality of sensors as the representative value. do,
A diagnostic device according to claim 1 . The processing unit divides the data of each unit period into a predetermined number and calculates the difference between the maximum value and the minimum value individually calculated, the average value Δ for each unit period, and all the values in the specific operation cycle Calculate the mean μ and standard deviation σ, and calculate the standardized value using the mean Δ, the mean μ and the standard deviation σ;
A diagnostic device according to claim 2. In the grouping process, the processing unit includes a range of a minimum value and a maximum value of a difference in magnitude of data variation in a specific unit period among the unit periods, and a range of data variation in other specific unit periods. If the range of the minimum value and the maximum value of the difference overlaps, the representative values are considered to be of the same level, and the same group number is given to each data of both unit periods.
A diagnostic device according to any one of claims 1 to 3. the sensor is a vibration sensor,
The time-series data is vibration level data of the equipment,
A diagnostic device according to any one of claims 1 to 4. A diagnostic method for diagnosing the state of equipment in which a plurality of processes exist in one operation cycle,
the computer
Among the time-series data of the plurality of sensors indicating the state of the equipment, the data from the start to the end of a specific operation cycle is divided into unit periods, and each unit period for the data of the entire period in the specific operation cycle. A calculation process for calculating a representative value indicating the relative relationship of the data for each unit period;
A grouping process of assigning a group number numbered in order of magnitude of the representative value to the data of each unit period;
an output process of outputting a screen of a graph showing the group numbers assigned to the data of each unit period, plotted in chronological order of each unit period, overlaid for a plurality of operation cycles;
diagnostic method. A diagnostic program for diagnosing the state of equipment in which multiple processes exist in one operation cycle,
to the computer,
Among the time-series data of the plurality of sensors indicating the state of the equipment, the data from the start to the end of a specific operation cycle is divided into unit periods, and each unit period for the data of the entire period in the specific operation cycle. A calculation process for calculating a representative value indicating the relative relationship of the data for each unit period;
A grouping process of assigning a group number numbered in order of magnitude of the representative value to the data of each unit period;
an output process of outputting a screen of a graph showing the group numbers assigned to the data of each unit period, plotted in chronological order of each unit period, overlaid for a plurality of operation cycles;
diagnostic program.

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