Creping process performance tracking and control

Abstract

The present disclosure relates to techniques for tracking data associated with a creping process. The techniques include measuring process data, where the process data includes vibration data, and where measuring process data includes measuring vibration data of a creping blade using one or more vibration sensors. The techniques also include assigning a score to the process data, including assigning a vibration score to the vibration data. The techniques also include generating an index score based on the score of the process data and the vibration score of the vibration data.

Description

[0001] Related matters

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 149,727, filed February 16, 2021, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to wrinkling, and more specifically to tracking wrinkling performance. Background Technology

[0004] In the tissue paper manufacturing process, the paper is dried on heated drying cylinders (called Yankees or Yankee cylinders). Crinkling is the process of impacting the paper against hard blades (called crease blades), thereby compressing the paper longitudinally and creating a folded paper structure. Crinkling breaks down many of the interfiber bonds in the paper, thus giving tissue paper its characteristic bulk, stretch, absorbency, and softness.

[0005] When the creping doctor blade wears out, it must be replaced with a new one. The blade replacement process is a significant source of downtime or production loss for tissue paper machines because creping products cannot be produced while the blade is being replaced.

[0006] During normal operation, the creaser tip travels within the coating on the surface of the drying cylinder and experiences minimal out-of-plane movement. However, if the amplitude of this out-of-plane movement becomes sufficiently high, the creaser will oscillate above and below the paper, resulting in a tremor that manifests as a transverse (CD) defect. Paper defects caused by this tremor will appear as multiple holes or a lace-like appearance on the CD.

[0007] Under severe vibration, the scraper will penetrate the Yankee coating and come into direct contact with the drying cylinder surface. If this occurs, it can potentially damage the drying cylinder surface, causing horizontal grooves on the metal surface. Once the drying cylinder surface is damaged, it can only be repaired by stopping production and re-grinding the surface. Re-grinding is a costly operation, resulting in production losses and process costs, and the reduced wall thickness negatively impacts the container pressure rating, thus shortening the drying cylinder's lifespan.

[0008] Excessive vibration on the creping doctor blade can lead to chatter conditions, which can be caused by mechanical and operational or process conditions. Examples of mechanical vibration sources include press rolls, pumps, felt, Yankee cylinder bearing, insufficient stiffness of the doctor blade fixture, etc., and dryer roundness distortion caused by thermal non-uniformity. Once the mechanical vibration source is identified, the maintenance intervention to correct the problem typically requires shutting down the equipment, resulting in production loss. In contrast, operational practices or process conditions that can affect vibration can include cleaning the paper machine clothing, changing the paper machine clothing vacuum or shower settings, changing the drying temperature, changing the hood temperature, using steam showers, changing the transfer roll pressure, changing the edge jet, changing the doctor blade type and edge bevel, engaging the cleaning blade, changing the cleaning or creping blade, paper moisture content, coating chemistry, machine speed, basis weight, furnish, and loading pressure, which can be addressed without interrupting production.

[0009] Regardless of the vibration source, excessive vibration experienced by the doctor blade can lead to chatter conditions, affecting product quality, machine runnability, and asset value. However, the dynamic behavior of the Yankee dryer operation can produce large variations in the sensor signals without reaching a chatter condition. Thus, data analysis becomes more complex compared to traditional wear and failure detection. SUMMARY

[0010] The present disclosure generally describes a method of tracking data associated with a creping process. In particular, examples herein can reduce the complexity of data generated by one or more sensors during a creping process. The reduction in creping data complexity can facilitate maintenance of portions of a creping machine and can further promote efficient operation of the creping process and result in a higher quality final product.

[0011] One example of such a method includes measuring process data, assigning a score to the process data, and generating an index score including the process data. The process data can include vibration data, and measuring process data can include measuring vibration of a creping blade using one or more vibration sensors. Assigning a score to the process data can then include assigning a score to the vibration data, where the generated index score includes at least the assigned score of the vibration data. Further, the method can include determining an average vibration value of the creping blade, where the average vibration value is used to assign the score to the vibration data.

[0012] In another example, assigning a score to the vibration data includes comparing the vibration data to previous vibration data. The method can also include outputting a signal to a display. In the method, the previous vibration data can be associated with at least one previously used creping blade. The method can also include reducing the previous vibration data to a single value per creping blade, and can also include comparing a standard deviation of the vibration data to a standard deviation of the previous vibration data, where the single value is the standard deviation of the previous vibration data.

[0013] In another example, reducing the previous vibration data to a single value for each creping blade includes generating fitted vibration data by fitting a function to the vibration data. The method can also include generating a detrending function by calculating a difference between the measured vibration data and the fitted vibration data, and can further include calculating a standard deviation of the detrending function.

[0014] In another example, the method includes assigning a score to the vibration data, which can include comparing the vibration data to a threshold value. The method can also include assigning a score to the at least one vibration value using a difference between the vibration data and the threshold value, with a greater difference between the vibration data and the threshold value corresponding to a worse score. The score can also be associated with a color, where the color corresponds to the difference between the vibration data and the threshold value. The generated index score can include the assigned score for the vibration data and at least one other score.

[0015] In another example, the method includes weighting the scores assigned to the vibration data, where the scores assigned to the vibration data are weighted based on operator input. The method can also include comparing the index score to a threshold value and outputting a signal if the index score is above or below the threshold value. Additionally, the method can include using the scores assigned to the vibration data to determine a length of time until the creping blade should be replaced.

[0016] In another example, the method includes using the generated index score to evaluate a length of time until the creping blade should be replaced. The method can also include using the index score to evaluate performance of the Yankee dryer. The method can also include using the index score to prevent damage to the Yankee dryer. Additionally, the method can include using the index score to determine performance of the creping process.

[0017] In another example, the generated index score includes measurements from one or more other sensors selected from the group consisting of a vibration sensor, a temperature sensor, a tachometer, a pressure sensor, a flow sensor, a humidity sensor, and any combination thereof. The method can include comparing the measurements of the one or more other sensors to a threshold value and outputting a signal when the measurements are above or below the threshold value.

[0018] In another example, the present disclosure relates to a system including a memory configured to store process data, where the process data includes vibration data. The system also includes one or more sensors configured to measure process data, where measuring process data includes measuring vibration data of a creping blade using one or more vibration sensors. The system further includes one or more processors configured to assign a score to the process data, including assigning a vibration score for the vibration data, and generate an index score based on the scores of the process data and the vibration score of the vibration data.

[0019] In another example, the present disclosure is directed to a non-transitory computer-readable storage medium having stored thereon instructions that, when executed, cause one or more processors of a computing device to measure process data, where the process data includes vibration data, and where measuring the process data includes measuring vibration data of a creping blade using one or more vibration sensors. The instructions also cause the one or more processors to assign scores to the process data, including assigning a vibration score to the vibration data; and generate an index score based on the scores of the process data and the vibration score of the vibration data. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a perspective view of an exemplary Yankee dryer having a doctor blade and a vibration sensor according to an aspect of the present disclosure.

[0021] Figure 2 is a cross-sectional view of an exemplary doctor blade assembly performing a creping process according to an aspect of the present disclosure.

[0022] Figure 3 is a graph of exemplary vibration data from an accelerometer according to an aspect of the present disclosure.

[0023] Figure 4 is a graph of exemplary average vibration data from a plurality of creping blades according to an aspect of the present disclosure.

[0024] Figure 5 is a graph of exemplary detrended standard deviation of vibration data from a plurality of creping blades including alarm levels according to an aspect of the present disclosure.

[0025] Figure 6A is an exemplary graph for scoring process data according to an aspect of the present disclosure.

[0026] Figure 6B is an alternative exemplary graph from scoring process data according to an aspect of the present disclosure.

[0027] Figure 7 is an exemplary index of process data including scores according to an aspect of the present disclosure.

[0028] Figure 8 is a block diagram illustrating an exemplary computing device configured to determine characteristics of received user input and output a corresponding set of child elements associated with an application on the computing device, in accordance with one or more aspects of the technology described in this disclosure.

[0029] Figure 9 is a flowchart illustrating an exemplary mode of operation of a processor performing creping process performance tracking and control according to an aspect of the present disclosure. Detailed Implementation

[0030] The following detailed description is exemplary in nature and is not intended to limit the scope, applicability, or configuration of the invention in any way. Rather, the following description provides some practical illustrations of examples for carrying out the invention. Examples of construction, materials, dimensions, and manufacturing processes are provided for selected elements, and all other elements use constructions, materials, dimensions, and manufacturing processes known to those skilled in the art. Those skilled in the art will recognize that many of the examples mentioned have various suitable alternatives.

[0031] from Figure 1 start, Figure 1 This is a perspective view of an exemplary assembly 100 used to crease paper material during a papermaking process. The assembly 100 includes a Yankee dryer 102 having a drive side and an operating side, a crease blade 104, a vibration sensor 106, uncreased paper 108, creased paper 110, and a tachometer 112.

[0032] In operation, uncreased paper 108 with a first moisture content can adhere to a coating on the Yankee dryer 102. Then, as the Yankee dryer rotates about its longitudinal axis, the Yankee dryer 102 can use heat (e.g., steam heat) from the Yankee dryer 102 to dry the paper to a second moisture content below the first moisture content. In addition to the Yankee dryer, the uncreased paper 108 with the first moisture content can also be dried by a gas hood that impinges hot air onto the surface of the paper not adhered to the Yankee dryer 102. Furthermore, the Yankee dryer and the gas hood can dry the paper simultaneously. Paper with a lower moisture content than the uncreased paper can then be removed from the Yankee dryer 102 by a crease blade 104. The crease blade 104 can travel over the surface of the Yankee dryer 102 within the coating covering it. For example, the crease blade 104 can press against the surface of the Yankee dryer 102 and penetrate into the coating sprayed onto it. In some examples, the crease blade 104 travels along the surface of the Yankee dryer cylinder 102. The crease blade 104 may also extend across the entire width of the Yankee dryer cylinder 102 from the operating side to the drive side. Furthermore, the crease blade 104 can compress the paper in the direction of machine rotation (indicated by arrow 114), thereby producing the folded paper structure of the crease paper 110. In some examples, more than one crease blade 104 is used during the crease process. In some examples, one or more blades may be used as cleaning blades to remove excess coating from the surface of the Yankee dryer cylinder 102 and prepare the cylinder for additional coating application. By creases, numerous interfiber bonds within the paper are broken, thus imparting the characteristic bulk, stretch, absorbency, and softness of tissue paper.

[0033] Various sensors can be used to monitor the drying and creping process of the uncreped paper 108. In Figure 1 In examples, a vibration sensor 106 is used, and it can be any type of sensor capable of detecting vibrations. In some examples, the vibration sensor 106 is an accelerometer, and in some examples, the accelerometer is a piezoelectric accelerometer. In operation, the vibration sensor 106 can be used to measure the vibrations of the creping blade 104 during the creping process. For example, as the creping blade travels over the surface of the Yankee dryer 102 (e.g., in the coating), the vibration sensor 106 can detect differences in the frequency and amplitude of the vibrations of the creping blade between the operating side (e.g., front side) and the drive side (e.g., back side) of the Yankee dryer. Differences in the frequency and amplitude of the vibrations can indicate that certain parts of the creping assembly need maintenance. Other sensors can also be used to monitor the drying and creping process of the uncreped paper. In Figure 1 In some examples, a tachometer 112 can measure the speed (e.g., revolutions per minute) at which the Yankee dryer 102 is rotating. In some examples, a pressure sensor is used, which can detect things such as the Yankee steam pressure and uhle box pressure (e.g., vacuum). In some examples, a sensor is used to measure the amount, type, and rate of coating that is sprayed onto the Yankee dryer 102. In some such examples, the amount and / or rate of adhesive coating that is sprayed onto the Yankee dryer 102 can be measured by a sensor, the amount and / or rate of release coating that is sprayed onto the Yankee dryer 102 can be measured by a different sensor, and a processor and / or another sensor can measure the ratio of the amount and / or rate of adhesive coating to release coating that is sprayed onto the Yankee dryer 102. Other sensors are contemplated, such as acoustic sensors, chemical sensors, electrical sensors, humidity sensors, flow sensors, position sensors, motion sensors, speed sensors, optical sensors, and other sensors that can measure various aspects of the components that can crepe the paper, and one of ordinary skill will recognize that the present disclosure is not limited to the listed sensors.

[0034] Turning to Figure 2 , Figure 2 is a cross-sectional view of an exemplary creping blade assembly 200 that performs a creping process according to one aspect of the present disclosure. The creping blade assembly 200 includes a creping blade 204 having one or more vibration sensors 206. The creping blade 204 and the one or more vibration sensors 206 are attached to a blade fixture 212. The creping blade 204 can travel over the surface of a Yankee dryer 202 in a coating 214 that is located on the surface of the Yankee dryer 202. In some examples, the creping blade 204 can travel along the surface of the Yankee dryer 202, and in some examples, the creping blade 204 can travel over the coating 214 that is located on the surface of the Yankee dryer 202. Figure 2Also included are uncreped material paper 208 and creped material paper 210 located on the surface of Yankee dryer 202.

[0035] In operation, creping blade assembly 200 performs a creping operation on uncreped material paper 208. As Yankee dryer 202 uses heat to remove moisture from uncreped material paper 208, the coating 214 of the surface of Yankee dryer 202 can cause uncreped material paper 208 to stick to Yankee dryer 202. Creping blade 204 can then be used to remove material paper 208 from the surface of Yankee dryer 202 and fold the material paper, resulting in creped material paper 210. During this process, creping blade 204 can vibrate against the surface of Yankee dryer 202, which can result in various undesirable outcomes. For example, excessive vibration of creping blade 204 can result in holes appearing in creped material paper 210, the coating 214 on the Yankee dryer surface becoming uneven, and can also result in damage to the Yankee dryer surface itself (e.g., under the coating). In some examples, excessive vibration can be considered chatter, as creping blade 204 can chatter against Yankee dryer 202. Measuring the vibration of creping blade 204 can help prevent excessive vibration, as when excessive vibration is detected, corrective measures can be taken to remedy any issues causing the excessive vibration. A method of detecting and addressing issues such as chatter using at least vibration data is discussed further herein. In some examples, a warning system can detect and address excessive vibration (e.g., chatter) of a creping blade. Some such examples and methods are described in U.S. Patent 9,404,895, filed October 20, 2011, entitled “METHOD FOR EARLY WARNING CHATTER DETECTION AND ASSET PROTECTION MANAGEMENT,” and U.S. Patent 10,604,896, filed August 1, 2016, entitled “METHOD FOR EARLY WARNING CHATTER DETECTION AND ASSET PROTECTION MANAGEMENT,” both of which are assigned to the patentee of the present application and are incorporated herein by reference in their entirety.

[0036] Continuing Figure 2The operation of the example of FIG. 1, when the creping blade 204 travels over the surface of the Yankee dryer 202 in the coating 214 on the Yankee dryer 202, the creping blade 204 can dull and need to be replaced. In some examples, the creping blade 204 must be replaced before it dulls because the blade can have defects or other issues that would affect the creping process and the quality of the creped material paper 210. The time between when the creping blade is first used and when it dulls or needs to be replaced can be considered the blade service life. There are many different ways that the need to replace the creping blade 204 can be monitored. In some examples, the vibration sensor 206 can measure vibration data of the creping blade 204 and send the vibration data to the processor. The processor can use the vibration data and compare the vibration data to a threshold that represents the point in time when the creping blade needs to be replaced. In some examples, visual measurements (e.g., inspections) of the creped material paper 210 can be used to determine that the creping blade 204 needs to be replaced. Further, in some examples, the processor can predict when the creping blade 204 needs to be replaced. The processor can use different methods to predict the need to replace the creping blade 204, including using historical data (e.g., previous blade service lives) and / or current data (e.g., from the vibration sensor 206).

[0037] Turning to Figure 3 , Figure 3 is a chart of example vibration data obtained by a vibration sensor associated with a creping blade over time. Figure 3 The vibration data of FIG. 1 is in the form of root mean square acceleration or gRMS. In this disclosure, the vibration data can include gRMS measurements. The vibration data can be used to determine the service life of a creping blade, which can be further used in a variety of applications as further described herein. Figure 3 The vibration data of FIG. 1 includes a number of different points in time during the life of a creping blade, with most of the points in time being obtained when the creping blade is running. When the creping blade is running, the vibration data can be higher than if the creping blade is not running. However, the vibration data also includes a number of points in time where the value is zero or near zero. In some examples, at such points in time, the creping blade is in the process of being replaced. As shown in FIG. 1, the time between points in time where the vibration value is zero or near zero can be considered the service life of the creping blade. Figure 3 In some examples, the processor can receive vibration data similar to the vibration data in FIG. 1 and can determine that the time between when the last value of the vibration data is zero or near zero (e.g., the start point) and when the first time the vibration data is zero or near zero (e.g., the stop point) corresponds to the service life of the creping blade. Figure 3 In some other examples, the processor can receive vibration data similar to the vibration data in FIG. 1 and can determine that the time between when the last value of the vibration data is zero or near zero (e.g., the start point) and when the first time the vibration data is zero or near zero (e.g., the stop point) corresponds to the service life of the creping blade. Figure 3The vibration data is similar to the vibration data in the original text, and when the vibration data changes from a higher value to a lower value (e.g., zero or near zero), it can be determined that the creased blade is no longer in use. Furthermore, when the vibration data changes from a lower value (e.g., zero or near zero) to a higher value, the processor can determine that the creased blade is in use after replacement. The threshold at which the vibration data is considered zero or near zero can be a creased blade replacement threshold. This threshold can have any value, but is preferably outside the operating range of the vibration data associated with the creased blade. In some examples, the vibration sensor measures the vibration of the creased blade and generates vibration data continuously, while in other examples, the vibration sensor measures the vibration of the creased blade and generates vibration data at discrete time points. Using vibration data to determine the lifespan of a creased blade is advantageous compared to other methods that use vibration data because it does not require a distributed control system (DCS) or electronic pressure sensors, both of which can be expensive or unavailable for older systems.

[0038] Other methods can also be used to determine the lifespan of the creased blade. In some examples, a sensor measures the loading pressure on the creased blade, and a processor compares the loading pressure to a threshold to determine whether the creased blade has been removed (e.g., replaced) and the time elapsed since then. In some such examples, the sensor measuring the loading pressure can generate a signal similar to... Figure 3 The vibration data is used to determine the shape of the creping blade and the loading data, and to determine if the creping blade is being replaced if the loading pressure changes from high to low, and if the creping blade is being reused if the loading pressure changes from low to high. In some examples, the sensor can measure the loading status of the creping blade and report a signal to the processor indicating whether the blade loading is enabled or disabled. In some such examples, the processor can measure the time between disable signals to determine the life of the creping blade. In some further examples, the operator operating the creping assembly can track the replacement of the creping blade in a logbook and determine the time between creping blade replacements. Other examples for determining the life of the creping blade are also considered.

[0039] like Figure 3As shown, the vibration data can include many discrete data points for a single creping blade, each measured at a particular time. The vibration data can be taken at any interval, but in some examples, the vibration data is taken at a rate of 60 measurements per hour or one measurement per minute. In some examples, a creping blade can have a useful life of as little as 2 hours in operation, and in some examples, a creping blade can have a useful life of more than 24 hours. It can be beneficial to track the vibration data for each creping blade used over time and to data analyze the total vibration data for all blades used over a period of time. For example, tracking the vibration data for multiple creping blades and trending the data can help to predict the useful life of future creping blades. However, the amount of vibration data generated by many creping blades can be difficult to analyze and computationally intensive. In some examples, a large amount of processing resources and memory resources (e.g., data storage devices) can be required to analyze the data. It can be desirable to reduce the amount of data generated by the vibration sensors in order to perform useful analysis of the generated data.

[0040] Turning to Figure 4 , Figure 4 is a graph of exemplary average vibration data for a series of creping blades. In Figure 4 , the vibration data for individual creping blades (e.g., as in Figure 3 ) has been reduced by taking the average of the vibration data as the creping blades are run. By reducing the vibration data for individual creping blades to a single data point (e.g., the average), the overall analysis of the data can be performed with less processing resources and memory resources. Additionally, an operator or processor can more easily draw conclusions from the data. For example, in Figure 4 , approximately 990 data points represent the average vibration data for approximately 990 creping blades. However, if the vibration data in Figure 4 was not reduced to an average vibration value for each creping blade, the number of data points would be greater than 260,000, requiring significantly more processing and memory resources.

[0041] Continuing Figure 4 , the vibration data can be analyzed, such as creating a trend line 416. In Figure 4In this model, trend line 416 uses the first approximately 200 crimping blade data points and extrapolates to approximately 700 blade data points. Trend line 416 indicates that the average vibration data increases over time as more crimping blades are used. In some examples, operators can use trend line 416 to diagnose problems with crimping blades or crimping assemblies. For example, some problems may include excessive vibration, excessive wear, improper crimping blade installation, crimping blade protrusion, incorrect edge beveling, or other issues. Trend line 416 may also be updated over time as the average vibration of each crimping blade is determined. Furthermore, an alarm and / or threshold level 418 for the average vibration can be set. In some examples, operators can use the alarm and / or threshold level 418 for the average vibration to determine when maintenance operations are required. In some examples, the alarm and / or threshold level for the average vibration can be updated over time. Additionally, in some examples, the alarm and / or threshold level 418 can be used in conjunction with trend line 416 to determine when maintenance operations are required. For example, the extrapolated trend line 416 may intersect with the alarm and / or threshold level 418 for the average vibration. Operators can be alerted and take corrective actions (e.g., maintenance) to prevent excessive average vibration of the wrinkling blades in the future, which could cause damage. In some examples, operators can use the intersection of trend line 416 with alarm and / or threshold levels 418 to determine when maintenance or changes to the wrinkling process are needed. This can be advantageous because operators can plan when the wrinkling process needs to be stopped and it also helps operators make decisions regarding various scheduling of the process.

[0042] Although Figure 4 Vibration data for each blade is reduced to a single average vibration value, but other techniques can be used to reduce the amount of vibration data. In some examples, reducing the vibration data for each blade to a single value is accomplished by calculating and / or determining a single value. In some examples, a single value may represent a maximum value, minimum value, pattern, changeover rate, standard deviation, detrended standard deviation, and blade lifespan. Other techniques for reducing vibration data and / or other data associated with the papermaking process are considered, and any combination of these techniques can be used to reduce the amount of data. In some examples, descriptive performance variables can be created by reducing a series of data to a single value. A descriptive performance variable can be a variable having values ​​that describe a series of values ​​such as measurement data.

[0043] In some examples, the vibration data of a creping blade is reduced to a detrended standard deviation of the vibration data. In such examples, vibration data is collected from one or more vibration sensors for a period of time (e.g., the life of the blade). A function can be fit to the vibration data, and a trend line can be created. In some examples, the function is a linear function, while in some examples, the function is a non-linear function. In some examples, the trend line can be considered the fitted vibration data. A difference between the fitted function and the measured vibration data can be calculated, which can result in data that is generally centered around 0. A standard deviation of the generally centered around 0 data can be taken, and the result can be considered a detrended standard deviation of the vibration data. Thus, the vibration data is reduced to a single value of the detrended standard deviation. The process of reducing the vibration data of a creping blade to a detrended standard deviation can be repeated for a series of creping blades. This can result in a single detrended standard deviation value for each blade. In some examples, this process can result in a single detrended standard deviation value for each side of the blade. For example, in Figure 5 , a detrended standard deviation can be determined for the front side of the creping blade and the back side of the creping blade. This can be advantageous compared to using a single detrended standard deviation for the entire creping blade, as it can be more accurate.

[0044] Continuing with the example of Figure 5 , detrended standard deviation values for the front and back sides of a plurality of creping blades over the life of the creping blades can be plotted. Further, in some examples, an alarm level can be set that can cause an alarm to be sounded and / or a signal to be output when the detrended standard deviation value exceeds the threshold. In some examples, the alarm and / or signal can be output to a display, such that an operator can be notified. The alarm level of the detrended standard deviation data can indicate something is wrong at a higher level compared to a traditional alarm that can indicate when current vibration data exceeds a threshold. For example, a traditional alarm can indicate that a creping blade is having problems due to high vibration measurements, while the alarm level can indicate that the creping blade is not the problem, but rather another part of the creping process is having problems, as new creping blades are still causing the detrended standard deviation of the vibration data to exceed the alarm level. In Figure 5 the example of , each data point represents a detrended standard deviation of vibration data associated with either the front side or the back side of a single creping blade. When the front side data is below the alarm level for all of the creping blades, the back side data is above the alarm level for a plurality of the creping blades until the point in time at which it is below the alarm level. This can indicate that other methods besides replacing the creping blade are being used to correct the creping process. In some examples, the alarm level can be used for a secondary alarm. In some such examples, the secondary alarm can have a higher sensitivity to the operating state of the creping process compared to a traditional alarm regarding the vibration data itself, and can be used to capture any defects in the process earlier and at a higher frequency.

[0045] While vibration data from a vibration sensor has been described, in some examples, the vibration data includes vibration frequencies. In some such examples, vibration data can be collected at various vibration frequencies. The vibration data can be separated into frequency bands, each of which represents a possible issue with the creping process that can be diagnosed by an operator. For example, vibration data in a frequency band of 0 kilohertz to 5 kilohertz can indicate that there is an issue with the creping blade fixture and / or that the Yankee dryer is out of round. Further, vibration data in a frequency band of 15 kilohertz to 20 kilohertz can indicate that there are no major issues and that the creping process is operating under acceptable conditions. In some examples, vibration frequency data from the creping blade can be reduced. In some examples, the vibration frequency data from the creping blade can be reduced on a per-blade basis in a similar manner as reducing the vibration data to a single value as described elsewhere herein.

[0046] While reducing vibration data from a creping blade to a single value has been discussed, reducing data from other sensors, machines, and components is also contemplated, and one of ordinary skill in the art will appreciate that the present disclosure is not limited to the examples of reducing variables provided. For example, reducing vibration data associated with a cleaning blade is also contemplated.

[0047] As described earlier herein, various sensors can be used to measure various portions of the creping assembly in the creping process. The sensors can include tachometers, pressure sensors, temperature sensors, humidity sensors, flow sensors, Yankee coating film thickness sensors, Yankee coating film modulus sensors, and other sensors, each of which is capable of providing operating data of the creping assembly to a processor. Some examples of such sensors are described in U.S. Patent 8,691,323 and U.S. Patent 9,388,530, the entireties of which are incorporated by reference herein. As many different sensors can be used for various portions of the creping process and / or a single portion of the creping process, the amount of data generated by the sensors can be large and complex. To reduce the amount and complexity of the data, scores can be assigned to the data generated from the sensors. Further, in some examples, the scores can be used to determine how various portions of the creping process are proceeding. Assigning scores to data generated from sensors (e.g., process data) can be done in a variety of ways and can also be based on a variety of factors. In some examples, assigning scores to data generated from sensors can be based on previous sensor data, statistical manipulation of the sensor data (e.g., mean and / or standard deviation), and / or any combination of factors.

[0048] In some examples, scores can be assigned to data generated from multiple sensors. In some examples, scores can be generated from data generated by multiple sensors. Scores can have a range of values. In some examples, scores can have a range between 0 and 100, where 100 represents an ideal score and 0 represents a least ideal (e.g., worst) score. For example, a pressure sensor can measure the Yankee dryer’s steam pressure in pounds per square inch (PSI) and a first measurement in PSI can be assigned a first score and a second measurement in PSI can be assigned a second score. In such examples, the second score can be closer to the ideal score of 100 than the first score and an operator can more easily interpret that the second measurement is better than the first measurement. By assigning scores to actual measurements, there is no need to know the ideal measurement (e.g., in PSI) and comparisons of measurements to thresholds can be made more quickly. In some examples, each score can be directly related to a sensor measurement, however in some examples, each score is not directly related to a sensor measurement. In some examples, the difference between a first value and a second value can be the same as the difference between a first score and a second score, however in some examples, the difference is not the same. In some examples, the assigned scores can indicate that the measured data is outside of an acceptable range.

[0049] While a single measurement from a sensor can be assigned a single score, in Figure 6A examples, a single score is assigned to a series of measurements (e.g., process data). Process data can include data from a creping machine and / or data from a product produced by a creping machine (e.g., creped paper). For example, process data can include vibration data of a creping blade, Yankee steam pressure, and quality data of creped paper. In some examples, process data is associated with a machine, such as a creping machine, and process data can be considered machine process data. Machine process data is process data associated with a respective machine. In some examples, process data can be logged, such as in non-volatile computer-readable memory. In some examples, logged process data can be used by one or more processors (e.g., to generate an index), as described elsewhere herein.

[0050] Assigning a score to a series of measurements can reduce the amount and complexity of measurements. For example, Figure 6Aincluding any sensor measurements over time, and is assigned a single score 67. To obtain the single score, the mean of the process data is determined as well as the standard deviation of the process data. The standard deviation of the process data is added to the mean to generate an upper limit value, while the standard deviation of the process data is subtracted from the mean to generate a lower limit value. The range between the upper limit value and the lower limit value of the process data can represent the variability of the process data. The upper limit value and the lower limit value are used with a control line (CL) because the score at which the upper limit value and the lower limit value intersect the control line represents the score assigned to the upper limit value and the lower limit value of the process data. In Figure 6A the example, the mean of the process data is 12.5, while the standard deviation is 1.5. Thus, the standard deviation added to the mean is 14 (e.g., the upper limit value), while the standard deviation subtracted from the mean is 11 (e.g., the lower limit value). The intersection of the upper limit value with the control line is approximately 35, so the score assigned to the upper limit value is 35. The intersection of the lower limit value with the control line is approximately 100, so the score assigned to the lower limit value is 100.

[0051] As Figure 6A can be seen in Figure 6A , the center value of the process data is approximately 10.8, while the standard deviation of the ideal process data is approximately 1. The standard deviation is then added to the center value once to determine the 1S point of the upper control line, added twice to determine the 2S point of the upper control line, and added a third time to determine the 3S point of the upper control line. A linear fit between the points can be determined, thereby determining the upper control line. To determine the lower control line, the standard deviation is subtracted from the center value once to determine the -1S point, subtracted twice to determine the -2S point, and subtracted a third time to determine the -3S point of the lower control line. A linear fit between the points can be determined, thereby determining the lower control line.

[0052] Continuing Figure 6A , once the upper limit value and the lower limit value of the process data are assigned, a total score can be assigned to the process data. To determine the total score of the process data, the higher score and the lower score are averaged. Thus, the total assigned score of the process data (including measurements from sensors over time) can be written as the following function:

[0053]

[0054] where Score Overall is the total assigned score, PRμ is the mean of the process data, σ is the standard deviation of the process data, and Score(x) is the assigned score as a function of x. In Figure 6AIn some examples, a total score 67 is determined by averaging a higher score 35 and a lower score 100, which are assigned to a standard deviation 14 added to the average process data and a standard deviation 11 subtracted from the average process data, respectively.

[0055] A process of assigning a total score to a range of values (e.g., process data variability) can be advantageous over assigning a score to each individual process data value, as individual values can not be indicative of the actual condition of the measured device. Instead, a process of assigning a total score can account for the variability of the measured data from a centerline. As a simple average does not account for the variability of the process data, this process can be more advantageous than employing a simple average of the process data.

[0056] In Figure 6A examples, a higher total score (e.g., closer to 100) of process data can indicate that the process data, sensor measurements, and components being measured by the sensor are in better condition than a lower score. In some examples, colors can be associated with ranges of assigned scores, and in some examples, red is associated with a range of total scores between 0 and 33, yellow is associated with a range of total scores between 34 and 66, and green is associated with a range of total scores between 67 and 100. Other ranges and colors are also contemplated. In some examples, lower scores correspond to worse operating conditions (e.g., excessive vibration).

[0057] In some examples, process data, such as vibration data, can have a desirable range, where one end of the range is not limited. In some such examples, it is of interest to add or subtract only one standard deviation from the average. For example, in Figure 6B , while vibration data above the average vibration data plus one standard deviation of vibration data is associated with a total score less than 100, the score for all vibration data below the average vibration data plus one standard deviation is 100. In Figure 6B , this is because lower vibration data values are classified as good operating conditions. In such examples, only one of the standard deviation of process data added to the average of process data or subtracted from the average of process data is of interest. Further, only one control line can be used to assign scores to such process data. For example, in Figure 6B , the standard deviation of vibration data added to the average of vibration data is approximately 12, which intersects the control line at a score of approximately 80. If more than one process data value is used to assign a score to process data, the total score can use the methods for total scores as previously disclosed herein.

[0058] In some examples, the process of assigning scores to process data can be repeated in order to assign scores to process data from multiple sensors. Further, in some examples, an index can be generated that includes multiple scores assigned to process data from multiple sensors. The index can be generated using a processor, an operator, a non-computer readable storage medium having instructions stored thereon, and / or other computing devices. In some examples, process data from one sensor can be given a separate score, while in some examples, process data from multiple sensors can be given separate scores.

[0059] In Figure 7 In some examples, process data from multiple sensors is combined into an index. The index can include a name of the process data that represents the type of process data. The index can also include a unit of measurement associated with the process data. The index can also include a color associated with each individual score as described above. For example, Figure 7 The index of Example 1 includes process data representing Yankee pressure (e.g., Yankee dryer pressure) with PSI units and an individual score of 65.70 associated with a yellow indicator. Further, Figure 7 The index of Example 1 includes process data representing Yankee pressure (e.g., Yankee dryer pressure) with PSI units and an individual score of 65.70 associated with a yellow indicator. Further,

[0060] The index can include scores associated with any sensors on the creping machine, and the index can be customizable such that scores associated with any sensors can be added or removed at any time. However, in some examples, in addition to or in lieu of scores associated with sensors on the creping machine, the index can include scores associated with sensors and / or tests performed on products produced by the creping machine. One such example of an index is a Yankee Protection Index. The Yankee Protection Index is so named because the types of process data included in the Yankee Protection Index can have an impact on the surface of the Yankee dryer. In this example, sources of process data include backside gRMS vibration data, frontside gRMS vibration data, Yankee steam pressure data, felt vacuum suction box pressure (e.g., vacuum level) data, adhesive coating addition data, release coating addition data, and data on the ratio of adhesive coating to release coating. Other sources of process data can be incorporated into the Yankee Protection Index, and sources of process data are not limited to those listed. Additionally, in some examples, not all of the listed sources of process data are included in the Yankee Protection Index.

[0061] Another example of an index is a blade performance index, which has sources of process data including blade life (e.g., determined from vibration data as described elsewhere herein), vibration data, blade load pressure, and edge wear rate. The blade performance index can be associated with the performance of a creping blade or other blade such as a doctor blade. In some examples, a crepe efficiency index can be an index of process data associated with how effectively a Yankee coating produces desired final quality properties in a final product (e.g., a tissue sheet). The crepe efficiency index can include process data from various sources including a stretch to crepe ratio, which is defined by the machine direction (MD) stretch in the finished paper divided by the crepe ratio, where the crepe ratio is defined by the reel speed minus the Yankee speed, divided by the reel speed. The crepe efficiency index can also include process data from sources such as the amount of Yankee adhesive coating, the amount of Yankee release coating, the ratio of Yankee adhesive coating to Yankee release coating, the crepe count in the paper (e.g., creped structures per inch), and vibration data. Another example of an index can be a product quality index, which is associated with the quality of a final product produced by a creping process (e.g., tissue quality). The product quality index can include sources of process data including paper moisture data, paper softness, paper bulk, paper stretch in the MD and cross direction (CD), and paper moisture after the Yankee dryer (e.g., crepe moisture). In some examples, the various sources of process data used in the product quality index are associated with the final product of the creping process (e.g., the final creped paper). In some examples, process data associated with product quality is considered product quality data. One of ordinary skill in the art will appreciate that other indices are considered, such as a felt health index, an energy usage index, and a wet end performance index. Such indices can include process data from various sources (e.g., sensors), and the process data included in each index can vary over time.

[0062] In some examples, an index includes at least a score associated with vibration data. In some examples, an index includes scores from different sources that do not affect each other (e.g., two different creping machines). In some examples, an index includes scores from sources that do affect each other. For example, in Figure 7In this index, the fractions of total vibration at the front and rear of the creping blade can be correlated and included in the index. By including fractions from mutually correlated sources, an index can be developed that incorporates factors most relevant to a specific process within the entire creping process. This can be advantageous because fractions from different sources can be grouped and are easier for the operator to understand compared to ungrouped fractions from different sources. The index can also reduce the time required to overhaul various aspects of the creping process, as fractions from mutually correlated sources can indicate which aspects(s) of the creping process require corrective action.

[0063] In some examples, different indices can reflect changes in each other, and in others, they can interact with each other. For example, a poor blade performance index can indicate the risk of the Yankee protection index. In such examples, if the blade performance index decreases, the Yankee protection index may also decrease. In some examples, the performance of one index can indicate the performance of another index, and each of the indices can include and weight the performance of another index.

[0064] In some examples, a total score can be assigned to the index. The total score can include scores from sources of difference within the index. In some examples, the total score can be a simple sum of the individual scores. In some examples, the total score can be a weighted sum of the individual scores. Figure 7 In the example, the total score of 77.54 is calculated based on a weighted sum of individual scores assigned to various sources of process data. Individual scores can be weighted in any way and can be modified at any time. In some examples, some individual scores have higher weights than others. In some such examples, the weights of individual scores may be based on the relative importance of the individual score to the total index. In some other examples, the weights of individual scores may be user-defined and / or based on variable sensitivity of the individual scores. For example, in... Figure 7 In the exponential score, the score related to the total vibration at the front of the creased blade has a weight of 36%, while the score related to Yanke pressure has a weight of 13%. Therefore, the total exponential score will reflect this weighting, and will depend more on the total vibration at the front of the creased blade than on the Yanke pressure score. Figure 7 In the example, the total vibration at the front of the wrinkling blade is more important to the wrinkling process than the Yanke pressure, so the total vibration at the front of the wrinkling blade has a higher weight than the Yanke pressure measurement.

[0065] exist Figure 7In some examples, the overall score for each individual score is determined by multiplying the individual score by a weight given to the individual score. The overall score for each individual score can be added together to generate an overall score for the index. In some examples, the overall score for the index can be considered an index score. For example, in Figure 7 In some examples, the overall index score 77.54 is the sum of the various chemical addition process data score multiplied by a weight of 38% (e.g., 0.38), the pre- and post-creping process total side vibration data score multiplied by a weight of 36% (e.g., 0.36), and the Yankee pressure and vacuum suction box vacuum level process data score multiplied by a weight of 13% (e.g., 0.13). In some examples, the index score includes at least a score for vibration data associated with one or more vibration sensors that sense vibration of the creping blade. Using a weighted index score can be advantageous because it can provide an easily understood overall indication of performance of a process involving multiple sensors. In some examples, the index score can include a color to help quickly understand how well the entire process including multiple data sources is performing. For example, in Figure 7 In some examples, a score of 77.54 is a green color indicating acceptable performance (e.g., scores 67 to 100), while scores 34 to 66 can be a yellow color indicating possible problems, and scores 0 to 33 can be a red color indicating possible failure.

[0066] In some examples, individual scores for process data can be compared to a threshold value, while in some examples, an index score can be compared to a threshold value. The threshold value can be a single value, a series of values, and / or a range of values. The threshold value can be defined at any time and can be changed at any time, however, in some examples, the threshold value is a user-defined value that remains constant over time. In some examples, an output signal and / or an alarm is triggered when a value exceeds the threshold value. In some examples, the value only has to be the same as the threshold value for the signal to be output and / or the alarm to be triggered. The signal and / or alarm can be output on a display, such that an operator can be notified that the value has exceeded the threshold value. The alarm can indicate that the value has exceeded the threshold value in any manner. For example, the alarm can be a visual alarm, an audible alarm, or a combination thereof. In some examples, the alarm is displayed on a display. In Figure 7In the example of FIG. 6, if any of the individual scores exceed a threshold score (e.g., 34), an alarm can be triggered, and additionally or alternatively, if the index score exceeds a threshold score, an alarm can be triggered. Alerting on the index score instead of the individual scores can be advantageous because the index score can allow an operator to determine which system of the creping assembly needs attention, instead of which individual sensor needs attention. In some examples, alerting on the index score allows an operator to isolate the problem to a particular system of the creping assembly. Additionally, in some examples, the individual scores of the process data can have greater variation over time, alerting on the index score more frequently than the individual scores. In some such examples, individual score alarms do not affect the entire creping process, and alarms on the individual scores would be unnecessarily frequent. Thus, alerting on the total score instead of the individual scores can be a better indication that corrective action is needed. The index score can also be compared to other index scores, such as historical index scores, index scores from other creping assemblies, and ideal index scores. Furthermore, the index score can be trended over time to determine how the index of the various measurement sources changes over time. This can provide insight into when maintenance or other corrective action should be scheduled.

[0067] While an index score corresponding to a creping process and / or creping assembly has been described, in some examples, an index score can be used for assemblies, machines, and / or other processes. Furthermore, in some examples, multiple index scores can be combined to form a total index having a total index score. For example, a papermaking assembly can include a creping assembly having an index and index score, and other assemblies and / or machines having other indices and index scores. The different indices and index scores can be combined together in any manner to form a single total index having a total index score representative of the entire papermaking assembly. This can be advantageous because the total index and index score can allow an operator to easily monitor the entire papermaking assembly due to the reduction in process data. Additionally, in some examples, the total index score can be easily compared to total index scores from other machines and / or assemblies, to an average of other total index scores, and / or to a normalized total score.

[0068] Figure 8 is a block diagram illustrating an example computing device configured to determine a characteristic of received user input and output a corresponding set of child elements associated with an application on the computing device in accordance with one or more aspects of the technology described in this disclosure. The computing device 810 is described below in Figure 8 FIG. 8. Figure 8 Only one particular example of the computing device 810 is shown, and many other examples of the computing device 810 can be used in other contexts and can include a subset of the components included in the example computing device 810 or can include additional components not shown in the example computing device 810. Figure 8 FIG. 8.

[0069] like Figure 8 As shown in the example, computing device 810 includes a user interface device (UID) 812, one or more processors 840, one or more communication units 842, one or more input units 844, one or more output units 846, and one or more storage units 848. UID 812 includes a display unit 802 and an presence-sensitive input unit 804. The storage unit 848 of computing device 810 includes a score module 820 and a regular data storage area 826.

[0070] One or more processors 840 may implement functions associated with computing device 810 and / or execute instructions associated therewith to generate an exponential fraction of wrinkling blades.

[0071] Examples of processor 840 include application processors, display controllers, auxiliary processors, one or more sensor hubs, and any other hardware configured to function as a processor, processing unit, or processing device. Module 820 can be operated by processor 840 to perform various actions, operations, or functions of computing device 810. For example, processor 840 of computing device 810 can retrieve and execute instructions stored in storage unit 848 that cause processor 840 to perform the operations described with respect to module 820. When executed by processor 840, these instructions can cause computing device 810 to generate an exponential fraction of the wrinkled blade. Fraction module 820 can perform operations for generating an exponential fraction based on data points received from sensor 852.

[0072] One or more storage units 848 within the computing device 810 may store information for processing during operation of the computing device 810 (e.g., the computing device 810 may store data accessed by module 820 during execution at the computing device 810). In some examples, the storage unit 848 is temporary memory, meaning that the primary purpose of the storage unit 848 is not long-term storage. The storage unit 848 on the computing device 810 may be configured to store information temporarily as volatile memory, and therefore the stored contents are not retained if power is lost. Examples of volatile memory include random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), and other forms of volatile memory known in the art.

[0073] In some examples, the storage component 848 also includes one or more computer- readable storage media. In some examples, the storage component 848 includes one or more non-transitory computer-readable storage media. The storage component 848 can be configured to store larger amounts of information than, for example, a volatile memory. The storage component 848 can further be configured for long-term storage of information as non-volatile memory space and retain information after power on / off cycles. Examples of non-volatile memories include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. The storage component 848 can store programs, instructions, and / or data associated with the modules 820 and the rules data store 826 (e.g., data). The storage component 848 can include a memory that can be configured to store data or other information associated with the modules 820 and the rules data store 826.

[0074] The communication channels 850 can interconnect each of the components 812, 840, 842, 844, 846, and 848 for inter-component communication (physically, communicatively, and / or operatively). In some examples, the communication channels 850 can include a system bus, network connection, inter-process communication data structures, or any other methods for communicating data.

[0075] One or more communication units 842 of the computing device 810 can communicate with external devices via one or more wired and / or wireless networks using one or more communication protocols. Examples of communication units 842 include a network interface card (e.g. an Ethernet card), an optical transceiver, a radio frequency transceiver, a GPS receiver, or any other type of device that can send and / or receive information. Other examples of communication units 842 can include short wave radios, cellular data radios, wireless network radios, and universal serial bus (USB) controllers.

[0076] One or more input components 844 of computing device 810 can receive input. Examples of input are tactile, audio, and video input. In one example, input components 844 of computing device 810 include a presence-sensitive input device (e.g., a touch-sensitive screen, a PSD), a mouse, a keyboard, a voice responsive system, a video camera, a microphone, or any other type of device for detecting input from a person or machine. In some examples, input components 844 can include one or more sensor components 852, one or more location sensors (GPS components, Wi-Fi components, cellular components), one or more temperature sensors, one or more motion sensors (e.g., accelerometers, gyroscopes), one or more pressure sensors (e.g., barometers), one or more ambient light sensors, and one or more other sensors (e.g., infrared proximity sensors, hygrometer sensors, etc.). In some examples, the one or more sensor components 852 can be a vibration sensor that can detect vibrations and / or accelerations of one or more creping blades. In some examples, the one or more sensor components 852 can be a tachometer for measuring a rotational speed of a component (e.g., a Yankee dryer), a pressure sensor for measuring a steam pressure of the Yankee dryer, a chemical sensor that can measure an amount and / or rate of adhesive coating sprayed onto the Yankee dryer, and / or a humidity sensor that can measure a moisture content of paper as it passes through various steps of a creping process. Other sensors (to name just a few other non-limiting examples) can include acoustic sensors, chemical sensors, electrical sensors, flow sensors, location sensors, motion sensors, speed sensors, and optical sensors.

[0077] One or more output components 846 of computing device 810 can generate output to a selected modality. Examples of modalities can include tactile notifications, auditory notifications, visual notifications, machine-generated voice notifications, or other modalities. In one example, output components 846 of computing device 810 include a presence-sensitive display, a sound card, a video graphics adapter card, a speaker, a cathode ray tube (CRT) monitor, a liquid crystal display (LCD), a light-emitting diode (LED) display, an organic LED (OLED) display, a virtual / augmented / extended reality (VR / AR / XR) system, a three-dimensional display, or any other type of device for generating output to a person or machine in a selected modality.

[0078] UID 812 of computing device 810 can include display component 802 and presence-sensitive input component 804. Display component 802 can be a screen, such as any of the displays or systems described with respect to output components 846, at which information (e.g., visual indications) is displayed by UID 812, while presence-sensitive input component 804 can detect objects at and / or near display component 802.

[0079] While illustrated as internal components of computing device 810, UID 812 also represents external components that share a data path with computing device 810 to transmit and / or receive input and output. For example, in one example, UID 812 represents a built-in component of computing device 810 (e.g., a screen on a mobile phone) that is positioned within and physically connected to an external package of computing device 810. In another example, UID 812 represents an external component of computing device 810 (e.g., a monitor, a projector, etc. that shares a wired and / or wireless data path with computing device 810) that is positioned outside of and physically separate from a package or housing of computing device 810.

[0080] UID 812 of computing device 810 can detect two-dimensional and / or three- dimensional gestures as input from a user of computing device 810. For example, sensors of UID 812 can detect movement (e.g., movement of a hand, arm, pen, stylus, haptic object, etc.) of a user within a threshold distance of sensors of UID 812. UID 812 can determine a two-dimensional or three-dimensional vector representation of the movement and correlate the vector representation to a gesture input having multiple dimensions (e.g., a wave, a pinch, a tap, a stroke, etc.). In other words, UID 812 can detect multi-dimensional gestures without requiring a user to make a gesture at or near a screen or surface of UID 812 that outputs information for display. Rather, UID 812 can detect multi-dimensional gestures performed at or near sensors that can or can not be located near a screen or surface of UID 812 that outputs information for display.

[0081] According to the techniques described herein, the one or more sensors 852 can measure process data, which can be electronically transmitted to the score module 820 of the computing device 810. The score module 820 can assign a score to the process data and can further generate an index score that includes the process data. In some examples, the process data includes product quality data, where the product quality data is associated with a final product of a creping process. However, in some examples, the process data includes vibration data and the one or more sensors 852 are vibration sensors that measure vibration data of a creping blade. In such examples, the score module 820 can assign a score to the vibration data, where the index score includes at least the assigned score of the vibration data. In some examples, the one or more processors 840 can determine an average vibration value of the creping blade and the score module 820 can assign a score to the vibration data using the average vibration value. In some examples, the one or more processors 840 calculate a standard deviation of the vibration data of the creping blade and the score module 820 assigns a score to the vibration data using the standard deviation of the vibration data. In some such examples, the standard deviation is compared to a threshold value and if the standard deviation exceeds the threshold value, then the computing device can output a signal indicating this condition. In further examples, the signal is output to a display.

[0082] In some examples, the score module 820 can compare the vibration data to previous vibration data and assign a score to the vibration data based on the comparison. Further, in some examples, the previous vibration data can be associated with at least one previously used creping blade, where the previous vibration data is reduced to a single value per creping blade in some examples. In some such examples, the one or more processors 840 can compare a standard deviation of the vibration data to a standard deviation of the previous vibration data, where the single value is the standard deviation of the previous vibration data. In some examples, the one or more processors 840 determine the single value by fitting a function to the vibration data, generating a detrended function by calculating a difference between the measured vibration data and the fitted vibration data, and calculating a standard deviation of the detrended function. In some examples, the one or more processors 840 determine a start point and a stop point of the at least one previously used creping blade, where the start point corresponds to a beginning of use of the at least one previously used creping blade and the stop point corresponds to an end of use of the at least one previously used creping blade.

[0083] In some examples, the vibration data includes vibration data at one or more frequencies. In some such examples, the computing device 810 can diagnose one or more issues with the Yankee dryer based at least in part on the vibration data. The issues with the Yankee dryer can include excessive vibration of the creping blade, scratching of the Yankee dryer surface, coating defects, steam pressure variations, and other incorrect operation of portions of the Yankee dryer system. In some examples, the score module 820 can assign a score to the at least one vibration value by comparing the vibration data to a threshold. In some such examples, the score module 820 assigns the score to the at least one vibration value using a difference between the vibration data and the threshold. In further examples, a larger difference between the vibration data and the threshold corresponds to a worse score. In some examples, the score module 820 associates a color with the score of the vibration data, which can correspond to the difference between the vibration data and the threshold.

[0084] In some examples, the index score includes the score assigned to the vibration data (e.g., by the score module 820) and at least one other score that can be associated with the papermaking process. In some such examples, the score module 820 associates the at least one other score with the creping blade chatter and can assign the at least one other score. However, in some examples, the score module 820 assigns scores to data from sensors such as: vibration of the back side of the creping blade, vibration of the front side of the creping blade, steam pressure of the Yankee dryer, felt vacuum suction box pressure (e.g., vacuum level), properties of the adhesive coating on the Yankee dryer, properties of the release coating on the Yankee dryer, a ratio of the properties of the adhesive coating and the release coating, and any combination thereof. In some examples, the one or more processors 840, in conjunction with the score module 820, weight any assigned scores (e.g., the score assigned to the vibration data) and add them together to provide the index score. In some such examples, an operator can input the weights into the computing device 810. In some examples, the one or more processors 840, in conjunction with the score module 820, can compare the index score to a threshold and output a signal if the index score is above or below the threshold.

[0085] In some examples, the one or more processors 840 can determine a length of time until the creping blade should be replaced using the score assigned to the vibration data by the score module 820 and / or by using the index score. In some examples, the one or more processors 840 use the index score to evaluate performance of the Yankee dryer. In some examples, the index score is used to prevent damage to the Yankee dryer and / or to determine performance of the creping process. Further, in some examples, the index score includes measurements from one or more other sensors optionally selected from the group consisting of: a vibration sensor, a temperature sensor, a tachometer, a pressure sensor, a flow sensor, a humidity sensor, and any combination thereof.

[0086] In some examples, the sensor 852 is a vibration sensor, which can be located on a front side of the creping blade and / or on a back side of the creping blade. In some examples, the creping blade is a doctor blade, while in some examples, the creping blade is a cleaning blade.

[0087] In some examples, the index score is associated with a first paper machine and is compared by the one or more processors 840 to a second index score, where the second index score is associated with a second paper machine. In some examples, the one or more processors 840 and / or the score module compare the index score to a value, which is an average index score of one or more paper machines. In some examples, the one or more vibration sensors generate vibration data, which includes a series of left-right variation measurements of the creping blade over time during a papermaking process.

[0088] Figure 9 is a flowchart showing an example mode of operation. Figure 9 The techniques of Figure 8 may be performed by one or more processors of a computing device, such as the computing device 810 shown. For illustrative purposes, the techniques of Figure 8 are described within the context of the computing device 810 Figure 9 but computing devices having a different configuration than the computing device 810 can also perform the techniques of Figure 9 .

[0089] In the example method of Figure 9 at 900, the one or more sensors 852 measure process data. At 910, the score module 820, alone or in combination with the one or more processors 840, assigns a score to the process data. At 920, the score module 820, alone or in combination with the one or more processors 840, generates an index score comprising the process data. Element 930 illustrates one example of the method outlined in steps 900, 910, and 920. In this example, measuring process data 900 includes measuring vibration data using one or more vibration sensors 940. Further, assigning a score to the process data 910 includes assigning a score to the vibration data 950. Additionally, in example 930, generating an index score comprising the process data 920 includes generating an index score comprising at least the assigned score of the vibration data 960.

[0090] It is recognized that depending on the example, certain acts or events of any of the techniques described herein can be performed in a different sequence, can be added, merged, or omitted altogether (e.g., all described acts or events can not be necessary to practice the techniques), and / or can be performed concurrently in any combination (e.g., multiple acts or events can be performed at one time). Moreover, in certain examples, acts or events can be performed by different parties and / or across multiple devices. Furthermore, the described techniques can be implemented by hardware, software, firmware, or any combination thereof. If implemented in software, the software can be executed in a computer device, such as the computing device 810 shown.

[0091] In one or more examples, the functions described can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions can be stored on or transmitted over as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media can include computer-readable storage media, which corresponds to a tangible medium such as data storage media, or communication media including any medium that facilitates transfer of a computer program from one place to another, e.g., according to a communication protocol. In this manner, computer- readable media generally can correspond to (1) tangible computer-readable storage media which is non-transitory or (2) a communication medium such as a signal or carrier wave. Data storage media can be any available media that can be accessed by one or more computers or one or more processors to retrieve instructions, code and / or data structures for implementation of the techniques described in this disclosure. A computer program product can include a computer-readable medium.

[0092] By way of example, and not limitation, such computer-readable storage media can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage, or other magnetic storage devices, flash memory, or any other storage medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any

[0093] Instructions can be executed by one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field-programmable arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Therefore, as used herein, the term "processor" can refer to any of the foregoing structures or any other structure suitable for implementing the techniques described herein. Additionally, in some aspects, the functionality described herein can be provided within dedicated hardware and / or software modules configured for encoding and decoding, or incorporated into combined codecs. Furthermore, the technology can be entirely implemented in one or more circuit or logic elements.

[0094] The techniques disclosed herein can be implemented in a variety of devices or apparatuses, including wireless mobile phones, integrated circuits (ICs), or a set of ICs (e.g., chipsets). Various components, modules, or units are described in this disclosure to emphasize functional aspects of a device configured to perform the disclosed techniques, but they do not necessarily need to be implemented through separate hardware units. Rather, as described above, various units may be combined in a codec hardware unit, or provided by a collection of cooperating hardware units including one or more processors as described above, along with appropriate software and / or firmware.

[0095] Various embodiments have been described. These and other embodiments are within the scope of the appended claims.

Claims

1. A method of tracking data associated with a creping process, the method comprising: measuring process data, wherein the process data comprises vibration data, and wherein measuring process data comprises measuring vibration data of a creping blade currently being used by a paper machine using one or more vibration sensors; assigning a score to the process data, including assigning a vibration score to the vibration data, wherein assigning a vibration score to the vibration data comprises: determining an average vibration value of the creping blade currently being used by the paper machine; calculating a standard deviation of the vibration data of the creping blade currently being used by the paper machine; and assigning the score to the vibration data includes being based at least in part on the average vibration value of the creping blade and the standard deviation of the vibration data of the creping blade; and generating an index score based on the score of the process data and the vibration score of the vibration data.

2. The method of claim 1, wherein the process data further comprises product quality data.

3. The method of claim 1, further comprising: comparing the standard deviation to a threshold value; and outputting a signal when the standard deviation of the vibration data exceeds the threshold value.

4. The method of claim 3, wherein outputting the signal comprises outputting the signal to a display when the standard deviation of the vibration data exceeds the threshold value.

5. The method of any of claims 3-4, wherein assigning the vibration score to the vibration data comprises comparing the vibration data to previous vibration data, wherein the previous vibration data is associated with at least one previously used creping blade.

6. The method of claim 5, further comprising reducing the previous vibration data to a single value per creping blade.

7. The method of claim 6, further comprising comparing the standard deviation of the vibration data to a standard deviation of previous vibration data, wherein the single value is the standard deviation of the previous vibration data.

8. The method of any of claims 6-7, wherein reducing the previous vibration data to a single value per creping blade comprises: generating fitted vibration data by fitting a function to the vibration data; generating a detrending function by calculating a difference between measured vibration data and fitted vibration data; and calculating a standard deviation of the detrending function.

9. The method of claim 5, further comprising determining a start point and a stop point of the at least one previously used creping blade, wherein the start point corresponds to a beginning of use of the at least one previously used creping blade and the stop point corresponds to an end of use of the at least one previously used creping blade.

10. The method of claim 1 or 2, wherein the vibration data comprises vibration data at one or more frequencies.

11. The method of claim 1 or 2, further comprising diagnosing one or more issues with a Yankee dryer based at least in part on the vibration data.

12. The method of claim 1 or 2, wherein assigning the vibration score to the vibration data further comprises: comparing the vibration data to a threshold value; and assigning the score to at least one vibration value using a difference between the vibration data and the threshold value, wherein a greater difference between the vibration data and the threshold value corresponds to a worse vibration score when the vibration data exceeds the threshold value.

13. The method of claim 12, wherein the vibration score of the vibration data is associated with a color, wherein the color corresponds to the difference between the vibration data and the threshold value.

14. The method of claim 1 or 2, wherein the index score comprises a combination of the vibration score of the vibration data and at least one other score of at least one other portion of the process data.

15. The method of claim 14, further comprising associating the at least one other score with one or more of a papermaking process and a creping blade chatter.

16. The method of any one of claims 14-15, wherein the at least one other score is a score of data describing one or more of: a vibration of a back side of a creping blade, a vibration of a front side of a creping blade, a steam pressure of a Yankee dryer, a vacuum suction box pressure of the Yankee dryer, a performance of an adhesive coating on a Yankee dryer, a performance of a release coating on a Yankee dryer, and a ratio of the performance of the adhesive coating and the release coating.

17. The method of claim 14, wherein generating the index score comprises assigning a respective weight to each of the vibration score and the at least one other score; and determining a sum of the weighted vibration score and the at least one other weighted score.

18. The method of claim 17, further comprising: determining wherein the weights assigned to the vibration data are based on operator input.

19. The method of claim 1 or 2, further comprising: comparing the index score to a threshold value; and outputting a signal indicating whether the index score is above or below the threshold value.

20. The method of claim 1 or 2, further comprising: using the vibration score assigned to the vibration data and one or more of the index score to determine a length of time until a creping blade should be replaced.

21. The method of claim 1 or 2, further comprising using the index score to determine an assessment of a performance of a Yankee dryer; and performing an action based on the assessment.

22. The method of claim 21, wherein the action comprises one or more of: using the index score to prevent damage to the Yankee dryer, determining a performance of the creping process.

23. The method of claim 1 or 2, wherein the one or more vibration sensors are located on one or more of a front side of a creping blade and a back side of a creping blade.

24. The method of claim 1 or 2, wherein the index score further comprises measurements from one or more other sensors, the one or more other sensors comprising one or more of: an additional vibration sensor, a temperature sensor, a tachometer, a pressure sensor, a flow sensor, and a humidity sensor.

25. The method of claim 24, further comprising: comparing the measurements of the one or more other sensors to a threshold value; and outputting a signal indicating whether the measurements are above or below the threshold value.

26. The method of claim 1 or 2, wherein the creping blade comprises a doctor blade.

27. The method of claim 1 or 2, wherein the index score is associated with a first paper machine, and wherein the method further comprises: comparing the index score to a second index score, the second index score being associated with a second paper machine.

28. The method of claim 1 or 2, further comprising comparing the index score to an average index score of one or more paper machines.

29. The method of claim 1 or 2, wherein the one or more vibration sensors generate the vibration data, the vibration data comprising a series of left-right variation measurements of the creping blade over time during a papermaking process.

30. A system for tracking data associated with a creping process, comprising: a memory configured to store process data, wherein the process data comprises vibration data; one or more sensors configured to measure the process data, wherein measuring process data comprises measuring vibration data of a creping blade currently being used by a paper machine using one or more vibration sensors; and one or more processors configured to: assign a score to the process data, including assigning a vibration score to the vibration data, wherein assigning a vibration score to the vibration data comprises: determining an average vibration value of the creping blade currently being used by the paper machine; calculating a standard deviation of the vibration data of the creping blade; and assigning the score to the vibration data comprises being based at least in part on the standard deviation of the vibration data of the creping blade currently being used by the paper machine and the average vibration value of the creping blade; and generate an index score based on the score of the process data and the vibration score of the vibration data.

31. The system of claim 30, wherein the one or more processors are further configured to perform the method of any of claims 1-29.

32. A non-transitory computer-readable storage medium having instructions stored thereon that, when executed, cause one or more processors of a computing device to: measure process data, wherein the process data comprises vibration data, and wherein measuring process data comprises measuring vibration data of a creping blade currently being used by a paper machine using one or more vibration sensors; assigning a score to the process data, including assigning a vibration score to the vibration data, wherein assigning a vibration score to the vibration data includes: determining an average vibration value of the creping blade currently being used by the paper machine; calculating a standard deviation of the vibration data of the creping blade currently being used by the paper machine; and assigning the score to the vibration data includes being based at least in part on the average vibration value of the creping blade and the standard deviation of the vibration data of the creping blade; and generating an index score based on the score of the process data and the vibration score of the vibration data.

33. The non-transitory computer-readable storage medium of claim 32, wherein the instructions, when executed, further cause the one or more processors to perform the method of any one of claims 1-29.

34. Use of the system of claim 30 or 31 to track data associated with a creping process.

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