Degradation diagnosis device for rolling equipment

Abstract

This degradation diagnosis device for rolling equipment includes an input / output data acquisition unit, a model identification unit, a monitoring parameter calculation unit, a monitoring parameter use determination unit, a representative value calculation unit, a representative value storage unit, and a degradation diagnosis unit. The monitoring parameter use determination unit has a category-specific monitoring parameter collection function of collecting monitoring parameters, calculated by the monitoring parameter calculation unit, for each category designated on the basis of the rolling conditions of a rolled material. The representative value calculation unit calculates a representative value for a set of monitoring parameters within a certain period for each category. The representative value storage unit accumulates, for each category, representative values in a learning period designated from the start of monitoring. The degradation diagnosis unit has a category-specific degradation determination function of determining the presence or absence of degradation for each category.

Description

Deterioration diagnosis device for rolling equipment

[0001] The present disclosure relates to a deterioration diagnosis device for rolling equipment that diagnoses deterioration of equipment installed in a rolling line.

[0002] In rolling lines where multiple types of equipment are installed, it is necessary to detect equipment deterioration (response deterioration) early, encourage operators to maintain the equipment, and stabilize operations and improve quality.To this end, there has been research into collecting and accumulating operating data from equipment and using it to detect equipment deterioration early.

[0003] For example, Patent Document 1 listed below discloses a condition abnormality detection device that monitors the condition of a robot (facility equipment) and supports robot maintenance. This device directly compares past time-series data (data in which the current values ​​flowing through a servo motor are arranged in chronological order) from normal times with current time-series data to obtain monitoring parameters, and then aggregates the obtained monitoring parameters daily to obtain a representative value. The obtained representative value is compared with the distribution of the monitoring parameters over a specified learning period to determine the deterioration of the actuator configured as a servo motor.

[0004] International Publication No. 2022 / 024946

[0005] However, in the rolling process, there is mutual interference between the equipment and the rolled material, and the equipment is affected by the rolled material. For example, if the equipment is a screw-down device (hydraulic cylinder), the screw-down force is affected by a reaction force from the rolled material. In the rolling process, the monitoring parameters change due to changes in the rolling conditions (steel type / plate thickness / plate width / target temperature, etc.) or changes in the rolling situation (plate speed of the rolled material set by temperature distribution, rolling load, etc.) even if the rolling conditions are the same. For this reason, it is difficult to accurately determine whether the equipment has deteriorated simply by directly comparing time-series data as described in Patent Document 1.

[0006] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a deterioration diagnosis device for rolling equipment that can accurately determine whether or not the equipment has deteriorated, even when there is mutual interference between the equipment and the rolled material that varies depending on the rolling conditions, etc.

[0007] A first aspect of the present disclosure relates to a deterioration diagnosis device for rolling equipment that determines whether or not equipment installed in a rolling line has deteriorated. The deterioration diagnosis device includes a data acquisition unit, a model identification unit, a monitoring parameter calculation unit, a monitoring parameter usage determination unit, a representative value calculation unit, a representative value storage unit, and a deterioration diagnosis unit. The input / output data acquisition unit acquires input / output data input to and output from the equipment during rolling each time a rolled material is rolled in the rolling line. The model identification unit identifies a mathematical model from the input / output data acquired by the input / output data acquisition unit and obtains parameters of the mathematical model. The monitoring parameter calculation unit calculates monitoring parameters from the parameters obtained by the model identification unit. The monitoring parameter usage determination unit has a category-specific monitoring parameter collection function that collects the monitoring parameters calculated by the monitoring parameter calculation unit by category specified by the rolling conditions of the rolled material. The representative value calculation unit calculates a representative value of a set of monitoring parameters within a certain period for each category obtained by the monitoring parameter usage determination unit. The representative value storage unit accumulates, for each category, the representative values ​​obtained by the representative value calculation unit during a designated learning period from the start of monitoring. The degradation diagnosis unit has a normal value distribution parameter calculation function that calculates a distribution parameter indicating the distribution of normal values ​​from a set of representative values ​​for each category accumulated in the representative value storage unit, and a category-specific degradation determination function that compares the representative value obtained by the representative value calculation unit after the learning period with the distribution parameter obtained by the normal value distribution parameter calculation function to determine whether or not degradation exists for each category.

[0008] The second aspect has the following characteristics in addition to the first aspect: The degradation diagnosis unit has a comprehensive degradation determination function that determines whether or not the facility equipment is degraded based on the determination results for each category obtained by the category-specific degradation determination function.

[0009] The third aspect has the following feature in addition to the second aspect: the comprehensive deterioration determination function calculates, for each fixed period of time, the proportion of the number of categories determined to be deteriorated among the number of categories determined to be deteriorated by the category-specific deterioration determination function, and determines that the facility equipment is deteriorated when the calculated proportion exceeds a threshold value.

[0010] The fourth aspect has the following characteristics in addition to the first aspect: the model identification unit uses a first-order or second-order ARX model as the mathematical model, and provides parameters of the mathematical model as coefficients of the ARX model; and the monitoring parameter calculation unit sets the monitoring parameter to a time constant or a damping coefficient.

[0011] The fifth aspect has the following feature in addition to the first aspect: the degradation determination device further includes a data usage determination unit that determines that the input / output data of the corresponding rolled material is unusable when the standard deviation of the input data acquired by the input / output data acquisition unit is smaller than a threshold value.

[0012] The sixth aspect has the following feature in addition to the first aspect: the deterioration determination device further includes a data usage determination unit that determines that the input / output data of the rolled material in question is unusable when a deviation between an average value of the input data and an average value of the output data acquired by the input / output data acquisition unit is equal to or greater than a threshold value.

[0013] The seventh aspect has the following feature in addition to the first aspect: the monitoring parameter usage determination unit further has an outlier exclusion function that calculates upper and lower limit values ​​from percentiles of the set of monitoring parameters for a certain period of time for each division obtained by the division-specific monitoring parameter collection function, and excludes monitoring parameters that fall outside the range of the upper and lower limit values ​​as outliers.

[0014] The eighth aspect has the same features as the seventh aspect, and further has the following characteristics: the representative value calculation unit provides, as the representative value, a median or average value of a set of parameters of the mathematical model within a certain period for each section after excluding outliers.

[0015] The ninth aspect has the following feature in addition to the first aspect: the representative value calculation unit has a degradation / failure date estimation function that accumulates the calculated representative values, plots them for each day, and calculates the date on which the threshold value will be exceeded from the intersection of a linearly or polynomially approximated line and a set threshold value.

[0016] A tenth aspect has the following characteristics in addition to the first aspect: The classification is specified by at least one rolling condition selected from the steel type of the rolled material, the target plate thickness, the target plate width, the target coiling temperature, whether or not a coil box is used, and the type of heating furnace.

[0017] The eleventh aspect has the following characteristics in addition to the first aspect: The normal value distribution parameter calculation function provides parameters indicating the distribution of normal values ​​by the mean value and standard deviation.

[0018] The twelfth aspect has the following characteristics in addition to the first aspect: ２ The method, or Shewhart control charts, or both, are used.

[0019] According to the present disclosure, by managing monitoring parameters obtained within a certain period of time by category specified by the rolling conditions and determining whether or not the equipment has deteriorated for each category, it is possible to provide a deterioration diagnosis device for rolling equipment that can accurately determine whether or not the equipment has deteriorated even when there is mutual interference between the equipment and the rolled material that varies depending on the rolling conditions.

[0020] Fig. 3 is a schematic diagram showing the configuration of a rolling line to which a deterioration diagnosis device for rolling equipment according to an embodiment is applied. Fig. 4 is a schematic diagram showing the configuration of a deterioration diagnosis device for rolling equipment according to an embodiment. Fig. 5 is a schematic diagram showing total length data which is input / output data acquired by an input / output data acquisition unit shown in Fig. 2. Fig. 6 is a schematic diagram showing functions possessed by a monitoring parameter use determination unit shown in Fig. 2. Fig. 7 is a schematic diagram showing functions possessed by a deterioration diagnosis unit shown in Fig. 2. Fig. 8 is a conceptual diagram showing an example of the hardware configuration of a processing circuit possessed by a deterioration diagnosis device for rolling equipment.

[0021] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. Common or corresponding elements in the various drawings will be denoted by the same reference numerals, and descriptions thereof will be simplified or omitted.

[0022] Fig. 1 is a schematic diagram showing the configuration of a rolling line to which a deterioration diagnosis device for rolling equipment according to an embodiment is applied. The rolling line 1 shown in Fig. 1 rolls steel or other metal material as a rolled material M into a plate shape in a hot working. The rolling line 1 is mainly equipped with a heating furnace 2, an edger 3, a roughing mill 4, a crop shear (not shown), a coil box (not shown), a finishing mill 5, a cooling device 6, and a winder 7.

[0023] The heating furnace 2 is configured to heat a slab, which is the rolled material M before rolling, to a predetermined temperature. The edger 3 is configured to shape the rolled material M into a predetermined plate width.

[0024] The roughing mill 4 has at least one, usually one to three, rolling stands (hereinafter also referred to as "stands"), and is configured to roll the rolled material M heated in the heating furnace 2 in a forward direction (from the upstream side to the downstream side of the rolling line) and a reverse direction (from the downstream side to the upstream side of the rolling line) for multiple passes. A crop shear (not shown) is disposed downstream of the roughing mill 4, and is configured to cut off any shape defects present at the leading or trailing end of the rolled material M using upper and lower blades based on the shape measured by a shape detector 81 (described later). A coil box (not shown) is disposed between the roughing mill 4 and the finishing mill 5, and is configured to temporarily wind up the roughly rolled rolled material M into a coil. A coil box need not be disposed in the rolling line 1.

[0025] The finishing rolling mill 5 is, for example, a hot tandem rolling mill. The finishing rolling mill 5 has multiple stands F1 to F7 (seven in this embodiment) arranged side by side in the conveying direction of the rolled material M. Each stand F1 to F7 is equipped with two upper and lower work rolls 51, two upper and lower backup rolls 52, and an electric motor 53 for roll rotation. The backup rolls 52 are provided with a screw-down device 54, such as a hydraulic cylinder, which is configured to adjust the roll gap between the upper and lower work rolls 51. The rolling load of each stand F1 to F7 is measured by a rolling load sensor 55. The rolling load sensor 55 is, for example, a load cell. The roll gap of each rolling stand F1 to F7 is measured by a gap sensor, such as a magnescale (not shown). Loopers (not shown) are arranged between adjacent stands to control the tension of the rolled material M between the stands.

[0026] The cooling device 6 is configured to be able to cool the rolled material M by injecting water onto the rolled material M using a cooling bank. The cooled rolled material M is wound into a coil by a winder 7.

[0027] Various sensors are installed as various measuring instruments at key points in the rolling line 1. Key points in the rolling line 1 include, for example, the outlet side of the heating furnace 2, the outlet side of the roughing mill 3, the outlet side of the finishing mill 5, and the inlet side of the winder 7. Various sensors may also be installed between stands F1 to F7 of the finishing mill 5. The various sensors include a shape detector 81 capable of measuring the shape (including width) of the rolled material M at the outlet side of the roughing mill 3, a thermometer 82 that measures the surface temperature of the rolled material M upstream of the finishing mill 5, a thickness gauge 83 that measures the actual thickness of the rolled material M at the outlet side of the finishing mill 5, the rolling load sensor 55 that measures the rolling load at each of the stands F1 to F7, and the gap sensor that measures the roll gap at each of the stands F1 to F7. The various sensors sequentially measure the state of the rolled material M and each piece of equipment.

[0028] The rolling line 1 is operated (commissioned) by a control system using a computer. The computer includes a host computer 10 and a process control computer 11, which are connected to each other via a network. An interface screen 12, which is an operation screen for an operator, is connected to the process control computer 11 via the network. The operator can perform operations such as inputting control conditions on the interface screen 12. The interface screen 12 can also serve as a screen display device DP, which will be described later.

[0029] The process control computer 11 executes setting calculations and controls of control targets in a series of rolling processes. The process control computer 11 also has a function of correcting the roll gap of each stand F1 to F7. Product information is input to the process control computer 11 from the host computer 10. The product information includes target information (product target) such as the target thickness (product thickness) and target width of the rolled material M heated in the heating furnace 2, as well as the steel type.

[0030] The process control computer 11 appropriately controls each piece of equipment based on the target information and the control conditions provided from the interface screen 12. The process control computer 11 is, for example, a controller. When the rolled material M is transported to a predetermined position on the rolling line 1, the process control computer 11 calculates settings for each piece of equipment that will achieve the target information, and operates, based on these setting values, the actuators and electric motors (rotating machines) of each piece of equipment, such as hydraulic cylinders that make up the screw down devices 54 of each of the stands F1 to F7.

[0031] 2 is a schematic diagram showing the configuration of a deterioration diagnosis device 9 that diagnoses (determines) deterioration of equipment (hereinafter also referred to as "rolling equipment") installed in the rolling line 1. The rolling equipment targeted by the deterioration diagnosis device 9 includes hydraulic cylinders used in the edger 3, roughing mill 4, and finishing mill 5, as well as electric motors (rotating machines) used in the edger 3, roughing mill 4, finishing mill 5, and looper. In this embodiment, an example will be described in which, of these rolling equipment, a hydraulic cylinder that is a reduction device 54 of the finishing mill 5 is targeted.

[0032] The degradation diagnosis device 9 has an input / output data acquisition unit 91, a data preprocessing unit 92, a data usage determination unit 93, a model identification unit 94, a model usage determination unit 95, a monitoring parameter calculation unit 96, a monitoring parameter usage determination unit 97, a representative value calculation unit 98, a representative value memory unit 99, and a degradation diagnosis unit 100.

[0033] A data storage device DB and a screen display device DP are connected to the degradation diagnosis device 9. The data storage device DB collects and stores a large number of items of data from devices used in the rolling process, such as the above-mentioned various sensors and the process control computer (controller) 11. The data storage device DB is, for example, a database. The screen display device DP displays the degradation diagnosis results (degradation determination results) that are output from the degradation diagnosis device 9. The data storage device DB and the screen display device DP may be provided inside the degradation diagnosis device 9.

[0034] The input / output data acquisition unit 91 extracts and acquires input data and output data (hereinafter referred to as "input / output data") for the target equipment from the numerous items of data stored in the data storage device DB. In this embodiment, the input / output data is a pair of a command value (input data) and an actual value (output data) regarding the position of the screw down device (hydraulic cylinder) 54 during rolling of each rolled material M. As shown in FIG. 3, the input / output data acquisition unit 91 extracts input / output data for the screw down device 54 from the data acquisition start time (the time when the rolled material M is engaged in the stand) to the data acquisition end time (the time when the rolled material M is removed from the stand). The input / output data extracted by the input / output data acquisition unit 91 in this manner is sometimes referred to as "total length data."

[0035] The data preprocessing unit 92 performs preprocessing on the total length data of each piece of equipment extracted by the input / output data acquisition unit 91. Specifically, from the total length data shown in FIG. 3, data on the unstable leading and trailing ends, which are in a transient state, is excluded. This prevents the data on the leading and trailing ends from being used for deterioration diagnosis. If the input data after excluding the data on the leading and trailing ends is x and the output data is y, the input data x and the output data y are defined by the following equations (1) and (2): Here, n is the number of data points shown in FIG.

[0036] The data usage determination unit 93 determines whether the input / output data preprocessed by the data preprocessing unit 92 is input data suitable for deterioration determination. For example, if the input data x preprocessed by the data preprocessing unit 92 has small fluctuations or if there is an offset between the input data x and the output data y, the data usage determination unit 93 determines that the input / output data of the rolled material M is not suitable for deterioration determination, and excludes the input / output data of the rolled material M from the targets of diagnosis.

[0037] Specifically, first, when the fluctuation of the input data x is small, that is, when the standard deviation σ of the input data x is small, ｘ is a predetermined reference value. σｘ If the fluctuation is smaller than 1 / f, the input / output data of the rolled material M is determined to be unusable, and the input / output data of the rolled material M is excluded from the diagnosis target. If the fluctuation of the input / output data is small, the data usage determination unit 93 cannot accurately acquire the dynamic characteristics of the equipment, which may impair the significance of the monitoring parameters described below that serve as indicators of the degree of deterioration.

[0038] Next, if there is an offset between the input data x and the output data y, the average value x of the input data x is ａｖｅ and the average value of the output data y ａｖｅ The deviation of the threshold θ ＯＦＳ If the above is true, the data usage determination unit 93 determines that the input / output data of the rolled material M in question is unusable, and excludes the input / output data of the rolled material M in question from the diagnosis target.

[0039] The model identification unit 94 identifies an ARX model from the input / output data of the rolled material M that was not excluded by the data use determination unit 93. The order of the ARX model includes first and second orders and can be selected for each equipment to be diagnosed. It is preferable to determine the order of the ARX model suitable for the equipment to be diagnosed in advance through experiments or simulations.

[0040] The first-order ARX model is expressed by the following equation (5). Here, a１１ is the model coefficient of the output data, b １１ denotes the model coefficient of the input data, and m denotes an arbitrary data point.

[0041] Model coefficient a to be identified １１ , b １１ is determined by the following equation (6) so that the sum of squared errors between the output data y and the calculated values ​​of the ARX model is minimized.

[0042] The second-order ARX model is expressed by the following equation (7). Here, a １１ , a ２２ is the model coefficient of the output data y, b １２ denotes the model coefficient of the input data x, and m denotes an arbitrary data point.

[0043] Model coefficient a to be identified １１ , a ２２ , b １２ is determined so that the sum of squared errors between the output data and the calculated values ​​of the ARX model is minimized.

[0044] The model usage determination unit 95 determines whether or not the ARX model can be used for response degradation diagnosis based on the sign of the coefficient of the ARX model obtained by the model identification unit 94. Specifically, when the first-order ARX model satisfies equation (9) and the second-order ARX model satisfies equation (10), it is not possible to calculate monitoring parameters using equations (11) or (12) described later, and therefore the model identification result of the rolled material M is excluded from the diagnosis target.

[0045] A monitoring parameter calculation unit 96 calculates monitoring parameters using the model coefficients of the rolled material M that were not excluded by the model use determination unit 95. As the monitoring parameters, a time constant τ is adopted in the first-order ARX model, and a damping coefficient ζ is adopted in the second-order ARX model.

[0046] In the case of a first-order ARX model, the model coefficient a １１ Calculate the time constant τ using Here, T ｓ indicates the sampling pitch.

[0047] In the case of a second-order ARX model, the model coefficient a １１ , a ２２ First, calculate the damping coefficient ζ using the following equation (12): ２２ Using the damping coefficient ζ and natural angular frequency ω ｎ Calculate the product of Here, T ｓ indicates the sampling pitch.

[0048] Next, the product ζω is calculated by the following equation (14): ｎ Using the natural angular frequency ω ｎ At this time, the calculation formula is θ calculated by the following formula (13): ζ Refer to and switch.

[0049] Finally, the natural angular frequency ω is calculated by the following equation (15): ｎ Calculate the damping coefficient ζ using

[0050] Fig. 4 is a schematic diagram showing the functions of the monitoring parameter use determination unit 97. The operation of the monitoring parameter use determination unit 97 will be described with reference to the flow shown in Fig. 4. The monitoring parameter use determination unit 97 has a category-specific monitoring parameter collection function 971 and an outlier exclusion function 972.

[0051] The classification-based monitoring parameter collection function 971 collects the monitoring parameters obtained by the monitoring parameter calculation unit 96, for example, daily, for each classification (also referred to as "class classification") specified by the rolling conditions. Here, the classification is specified by at least one rolling condition selected from the steel type, target plate thickness, target plate width, target coiling temperature, whether or not a coil box is used, and type of heating furnace. By managing the monitoring parameters for each classification specified by the rolling conditions in this way, it is possible to prevent the monitoring parameters from being affected by the rolled material (material) M.

[0052] The outlier exclusion function 972 excludes outliers from the monitoring parameters for one day collected by the category-specific monitoring parameter collection function 971. Of the set of monitoring parameters x for one day for each category, a monitoring parameter x that does not satisfy the following formula (16) is determined to be an outlier. An outlier is a sudden value or a value affected by manual intervention by an operator of the rolling line 1. Such outliers reduce the accuracy of deterioration determination, and are therefore excluded by the outlier exclusion function 972.

[0053] Here, P ７５ is the 75th percentile of the monitored parameters for one day, P ２５ is the 25th percentile of the monitored parameter for one day, and α is an arbitrary scaling factor.

[0054] The representative value calculation unit 98 calculates a representative value of the set of monitoring parameters for each category within a certain period that was not excluded by the monitoring parameter usage determination unit 97. Specifically, the representative value calculation unit 98 calculates the average or median of the set of monitoring parameters for each category within a certain period, and assigns the calculated average or median as the representative value. In this case, if the monitoring parameter usage determination unit 97 excludes outliers, a bias in the distribution of the monitoring parameters may occur if the number of rolled pieces M in each category per day is small, which may result in an erroneous diagnosis of deterioration. In such a case, the representative value for the corresponding day may not be calculated. Alternatively, the representative value calculation unit 98 may be provided with a degradation failure date estimation function that plots the obtained representative values ​​for each day, calculates the day on which the threshold value will be exceeded from the intersection of a linearly or polynomially approximated line with a set threshold value, and predicts the day on which deterioration will occur.

[0055] The representative part storage unit 99 stores the representative values ​​obtained by the representative value calculation unit 98 for each category, using a predetermined number of days from the monitoring start date as a learning period. At this time, the number of stored representative values ​​may be counted for each category, and the period until representative values ​​for the specified number of days are stored may be used as the learning period.

[0056] 5 is a schematic diagram showing the functions of degradation diagnosis unit 100. The operation of degradation diagnosis unit 100 will be described with reference to the flow shown in FIG.

[0057] The deterioration diagnosis unit 100 has a normal value distribution parameter calculation function 101 , a section-by-section deterioration determination function 102 , and a comprehensive deterioration determination function 103 .

[0058] The normal value distribution parameter calculation function 101 obtains a set of representative values ​​for each day of the learning period (e.g., Xth day of month X to Yth day of month Y) for each division stored in the representative portion storage unit 99. From the set of representative values ​​for each day of the learning period for each division obtained from the representative portion storage unit 99, the normal value distribution parameter calculation function 101 calculates, for example, the average value and standard deviation of the representative values ​​for each day of the learning period as distribution parameters indicating the distribution of normal values.

[0059] The section-by-section deterioration determination function 102 checks (compares) the representative value obtained by the representative value calculation unit 98 after the learning period with the distribution parameters obtained by the normal value distribution parameter calculation function 101 to determine whether or not deterioration exists for each section. There are many methods of this type of determination, but the section-by-section deterioration determination function 102 uses, for example, Hotelling's T ２ The determination can be made using the FTIR method, or Shewhart control charts, or both.

[0060] First, Hotelling's T ２ Assuming that the population of monitoring parameters follows a normal distribution, the representative value x on the jth day is ｒｅｐ (j) Average value of the representative values ​​for each day of the study period x ｒｅｐ，ａｖｅ , standard deviation σ ｘ＿ｒｅｐ The abnormality degree H on the jth day (for example, month Y, day Z) is calculated using the above formula.

[0061] An arbitrary threshold is set for the degree of abnormality H, and if the threshold is exceeded, it is determined that there is deterioration. It has been theoretically proven that the degree of abnormality H follows a chi-squared distribution with one degree of freedom, and the probability that H will take on a certain value can be calculated. For example, the probability that H = 3.84 is approximately 5%, the probability that H = 6.63 is approximately 1%, and the probability that H = 10.8 is approximately 0.1%. The threshold for determining there is deterioration can be set by referring to the relationship between the abnormal value H and the probability.

[0062] Next, the judgment method using the Shewhart control chart will be explained. ｒｅｐ，ａｖｅ The standard deviation σ in the positive and negative directions based onｘ＿ｒｅｐ The threshold is a constant multiple of x, and the representative value x on the jth day (for example, the Zth day of the Yth month) is ｒｅｐ If (j) is outside the range of the threshold, it is determined that there is deterioration.

[0063] Another method of determination that can be used in the category-specific deterioration determination function 102 is to use a threshold value that is a constant multiple of the average value of the representative values ​​for each day of the learning period, and determine that deterioration has occurred if the representative value exceeds the threshold value. Also, multiple threshold values ​​may be prepared for one determination method, and the degree of deterioration may be determined in stages.

[0064] The comprehensive deterioration determination function 103 makes a comprehensive determination (final determination) of whether or not the facility equipment is deteriorated based on the deterioration diagnosis results (determination results) for each category obtained by the category-specific deterioration determination function 102. Specifically, for each day, the proportion of categories determined to be deteriorated among the categories determined to be deteriorated by the category-specific deterioration determination function 102 on that date is calculated, and if the proportion exceeds a threshold, the equipment is determined to be deteriorated. In this way, by having the comprehensive deterioration determination function 103, deterioration can be determined with high accuracy even when there are a large number of categories.

[0065] The results of the assessment for each category by the category-specific deterioration assessment function 102 may be weighted equally and assessed comprehensively, or the results of the assessment for each category may be weighted differently and assessed comprehensively. For example, the results of the assessment for a category of steel types with a large number of rolling processes or for steel types that require strict assessment may be weighted heavily and assessed comprehensively, thereby enabling even more accurate deterioration assessment.

[0066] As described above, according to this embodiment, the monitoring parameters obtained by the data monitoring parameter calculation unit 96 are managed by the monitoring parameter usage determination unit 97 for each category specified by the rolling conditions at regular intervals, and the deterioration diagnosis unit 100 determines whether or not the equipment has deteriorated for each category. This makes it possible to provide a deterioration diagnosis device 9 that can accurately determine whether or not the equipment has deteriorated, even when there is mutual interference between the equipment and the rolled material M that varies depending on the rolling conditions, etc. Furthermore, by using the determination results for each category in the overall deterioration determination function 103 to make an overall determination, accurate determination is possible even when there are a large number of categories. Furthermore, by selecting input / output data suitable for deterioration determination in the data usage determination unit 93 and excluding outliers from the set of monitoring parameters for each category using the outlier exclusion function 972, even more accurate determination is possible.

[0067] Next, a specific structure of the degradation diagnosis device 9 will be described. There are no limitations on the specific structure of the degradation diagnosis device 9, and the following may be used as an example. FIG. 6 is a conceptual diagram showing an example of the hardware configuration of a processing circuit included in the degradation diagnosis device 9. Each unit and function constituting the degradation diagnosis device 9 is realized by the processing circuit. For example, the processing circuit includes at least one processor 90a and at least one memory 90b. For example, the processing circuit includes at least one dedicated hardware 90c. As a specific example, the processing circuit is a personal computer (PC) or the like.

[0068] When the processing circuit includes a processor 90a and a memory 90b, each function of the degradation diagnosis device 9 is realized by software, firmware, or a combination of software and firmware. At least one of the software and firmware is written as a program. At least one of the software and firmware is stored in the memory 402. The processor 90a realizes each function by reading and executing the program stored in the memory 90b. The processor 401 is also referred to as a CPU (Central Processing Unit), central processing unit, processing device, arithmetic unit, microprocessor, microcomputer, or DSP. For example, the memory 402 is a non-volatile or volatile semiconductor memory such as RAM, ROM, flash memory, EPROM, or EEPROM, a magnetic disk, a flexible disk, an optical disk, a compact disk, a minidisk, or a DVD.

[0069] When the processing circuit includes dedicated hardware 90c, the processing circuit may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof. For example, each function may be realized by a separate processing circuit. For example, each function may be realized collectively by a processing circuit. Alternatively, some of the functions may be realized by dedicated hardware 90c, and other parts may be realized by software or firmware. In this way, the processing circuit realizes each function by hardware 90c, software, firmware, or a combination thereof.

[0070] Although the embodiments of the present disclosure have been described above, the present disclosure is not limited to the above embodiments and can be implemented in various modifications without departing from the spirit of the present disclosure. For example, in the above embodiments, the hydraulic cylinder constituting the screw down device 54 of the finishing rolling mill 5 is described as an example of the target control device, but the present disclosure is not limited to this and may be installed in a hot rolling line.

[0071] Furthermore, when the number, quantity, amount, range, etc. of each element is mentioned in the above embodiments, the present disclosure is not limited to the mentioned numbers unless otherwise specified or clearly specified in principle. Furthermore, the structures, etc. described in the above embodiments are not necessarily essential to the present invention unless otherwise specified or clearly specified in principle.

[0072] 1...rolling line, M...rolled material, 5...finishing rolling mill, F1 to F6...rolling stand, 54...thread reduction device, 9...deterioration diagnosis device, 91...input / output data acquisition unit, 92...data pre-processing unit, 93...data use determination unit, 94...model identification unit, 95...model use determination unit, 96...monitoring parameter calculation unit, 97...monitoring parameter use determination unit, 971...category-specific monitoring parameter collection function, 972...outlier exclusion function, 98...representative value calculation unit, 99...representative part storage unit, 100...deterioration diagnosis unit, 101...normal value distribution parameter calculation function, 102...category-specific deterioration determination function, 103...overall deterioration determination function

Claims

1. A deterioration diagnosis device for rolling equipment installed in a rolling line for determining the presence or absence of deterioration of equipment, comprising: an input / output data acquisition unit for acquiring input / output data input / output to / from the equipment during rolling each time a rolled material is rolled in the rolling line; a model identification unit for identifying a mathematical model from the input / output data acquired by the input / output data acquisition unit and acquiring parameters of the mathematical model; a monitoring parameter calculation unit for calculating monitoring parameters from the parameters acquired by the model identification unit; a monitoring parameter usage determination unit having a category-specific monitoring parameter collection function for collecting the monitoring parameters calculated by the monitoring parameter calculation unit by category specified by the rolling conditions of the rolled material; a representative value calculation unit for calculating a representative value of a set of the monitoring parameters within a certain period of time for each category acquired by the monitoring parameter usage determination unit; and a memory unit for storing, for each category, the representative values ​​acquired by the representative value calculation unit during a specified learning period from the start of monitoring. a deterioration diagnosis unit having a normal value distribution parameter calculation function that obtains a distribution parameter indicating a distribution of normal values ​​from a set of the representative values ​​for each category accumulated in the memory unit, and a category-specific deterioration determination function that determines the presence or absence of deterioration for each category by comparing the representative value obtained by the representative value calculation unit after a learning period with the distribution parameter obtained by the normal value distribution parameter calculation function.

2. A deterioration diagnosis device for rolling equipment as described in claim 1, wherein the deterioration diagnosis unit has an overall deterioration judgment function for judging whether or not the equipment has deteriorated based on the judgment results for each category obtained by the category-specific deterioration judgment function.

3. A deterioration diagnosis device for rolling equipment as described in claim 2, wherein the comprehensive deterioration judgment function calculates, for each fixed period of time, the proportion of the number of categories judged to be deteriorated among the number of categories judged to be deteriorated by the category-specific deterioration judgment function, and judges the equipment to be deteriorated when the proportion exceeds a threshold value.

4. A deterioration diagnosis device for rolling equipment as described in claim 1, wherein the model identification unit uses a first-order or second-order ARX model as the mathematical model, and the parameters of the mathematical model are given by coefficients of the ARX model, and the monitoring parameter calculation unit sets the monitoring parameters to time constants or damping coefficients.

5. A deterioration diagnosis device for rolling equipment as described in claim 1, further comprising a data usage determination unit that determines that the input / output data for the corresponding rolling material is unusable when the standard deviation of the input data acquired by the input / output data acquisition unit is smaller than a threshold value.

6. A deterioration diagnosis device for rolling equipment as described in claim 1, further comprising a data usage determination unit that determines that the input / output data for the corresponding rolled material is unusable when the deviation between the average value of the input data and the average value of the output data acquired by the input / output data acquisition unit exceeds a threshold value.

7. The deterioration diagnosis device for rolling equipment as described in claim 1, wherein the monitoring parameter usage determination unit further has an outlier exclusion function that calculates upper and lower limit values ​​from percentiles of the set of monitoring parameters within a certain period of time for each of the categories obtained by the category-specific monitoring parameter collection function, and excludes monitoring parameters that fall outside the upper and lower limit values ​​as outliers.

8. The deterioration diagnosis device for rolling equipment as described in claim 7, characterized in that the representative value calculation unit gives the median or average value of the set of parameters of the mathematical model within a certain period of each category after excluding the outliers as the representative value.

9. A deterioration diagnosis device for rolling equipment as described in claim 1, wherein the representative value calculation unit has a deterioration failure date estimation function that accumulates the calculated representative values, plots them by day, and calculates the date on which the threshold value will be exceeded from the intersection of a linearly or polynomially approximated line and a set threshold value.

10. The deterioration diagnosis device for rolling equipment according to claim 1, wherein the classification is specified by at least one rolling condition selected from the steel type of the rolled material, the target plate thickness, the target plate width, the target coiling temperature, whether or not a coil box is used, and the type of heating furnace.

11. The deterioration diagnosis device for rolling equipment according to claim 1, wherein the normal value distribution parameter calculation function calculates a mean value and a standard deviation as the distribution parameters.

12. The above-mentioned deterioration judgment function is Hotelling's T ２ 2. The deterioration diagnosis device for rolling equipment according to claim 1, wherein the deterioration diagnosis device uses a Shewhart control chart, or a Shewhart control chart, or both of them.

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