Patent Description:
Color has been known to be a relevant parameter of yarn. Hence, it may be measured in order to assess yarn quality.

In particular, sections of yarn not matching a desired color may be removed during the rewinding process in a yarn clearer. While such yarn clearing improves overall yarn quality, it leads to loss of time and material.

<CIT> describes a yarn winding system having yarn monitoring devices detecting yarn color defects.

<CIT> describes a textile fiber processing plant having cameras for detecting color changes.

Hence, it is a general object of the invention to provide a method and apparatus of the type above that allow a better control the manufacturing process. Such better control may e.g. be obtained by an improved control or assessment of the process. In particular, the problem to be solved is to obtain a better understanding of the types of defects that may be present, and/or to judge if a given yarn fault is to be removed from the yarn or not.

This object is achieved by the method and apparatus of the independent claims.

Accordingly, the invention relates to a method for controlling or assessing a yarn manufacturing process in a yarn manufacturing apparatus having at least a first sensor head. The apparatus may e.g. be a spinning mill or a part thereof. Advantageously, the apparatus comprises several sensor heads, but some aspects of the present invention, e.g. some of the grouping, clustering, and/or correlation steps described below, can also be implemented with only a single sensor head as well.

The method comprises at least the following steps:.

The method is further characterized in that values that depend on the color parameter are clustered into color clusters.

In particular, the values can be used for at least one of the following measures:.

The time-series may be "formed" on the fly, i.e. its values may not be stored permanently but e.g. be subject to immediate statistical pre-processing. Alternatively or in addition thereto, the measured values of the time-series may be stored permanently.

Advantageously, the method may comprise the steps of determining, from a time variation of the color parameter, at least one variation parameter and using this variation parameter for controlling or assessing the process: The variation parameter may e.g. be derived from the statistical variance of the values of the parameter, and/or it may be descriptive of color clusters formed by the values of the color parameter.

Advantageously, the method may further include the following steps:.

In a particularly advantageous embodiment, the values of the operating parameter are permanently stored as part of the time-series of values, which allows for a later processing of the values.

Advantageously, for better comparing the values of the color parameter(s) and the values of the operating parameter(s), the first plurality of times (at which the color parameter is known) and the second plurality of times (at which the operating parameter is known) extend over at least a common time interval, i.e. the time ranges of the two pluralities of times overlap.

As mentioned, the operating parameter(s) may comprise at least one machine setting(s). For example, a given operating parameter may be one of the following:.

Using such a machine setting allows determining if the color parameter depends on the operating state of a processing section. For example, it may thus be found that operating a processing section might have a negative impact on another section of the apparatus.

The apparatus may comprise a plurality of individual processing sections, in which case the method can comprise the step of determining, for several of the processing sections, individual operating parameters. The parameters are individual in the sense that each parameter is descriptive of the operation at an individual processing section. Storing such individual operating parameters allows providing better insight into which processing sections have an influence on the values of the color parameter(s).

In a particularly important application, the operating parameter(s) and the color parameter(s) are correlated, i.e. it is assessed if the behavior, over time, of the color parameter(s) correlates with the operating parameter(s). This provides insight into how one or more operating parameters affect the color parameter(s).

For example, it may be found that the color parameter of a given elongate textile body may be correlated with the operating parameter of one or more processing sections that are not processing the given elongate textile body. This is particularly important for determining cross-contamination. For example, when processing a yarn of a given color and monitoring its color parameter, color faults may be correlated with the operation of sections of the apparatus processing a different yarn. In that case, it may be concluded that cross contamination is occurring between the section processing the given yarn and other sections processing other types of yarn.

According to the invention, the method comprises the step of grouping values that depend on the color parameter into color groups. This provides a better understanding of the types of defects that may be present, and/or it allows to judge if a given yarn fault is to be removed from the yarn or not.

The "values that depend on the color parameter" may be the individually measured values of the color parameter of values derived therefrom, e.g. values derived from a plurality of the measured values, for example by means of averaging.

The grouping is implemented by clustering, i.e. by an algorithm that does not require an a-priory knowledge of the groups used for grouping but may determine at least a subset of these groups (clusters) based on the values of the color parameter, e.g. by analyzing the distribution of the values of the color parameter. The clusters may be determined from starting parameters describing expected locations of the clusters, wherein at least some of the starting parameters are then refined to better match the measured values. Alternatively, the clusters may be determined based on the measured values alone, without using starting parameters for the cluster coordinates.

Hence, the method may comprise the step of clustering the values depending on the color parameter into clusters. Such clusters adapt to the current data, e.g. to the contaminants present at the site of the apparatus.

In this context, "clustering" into color clusters is the task of grouping the values of the color parameter in such a way that color values in the same group (called a cluster) are more similar to each other than to those in other groups (clusters). Similarity may e.g. be determined by a metric in the used color space, where the similarity increases as the distance derived from the metric decreases.

As mentioned, detecting cross contamination is an advantageous application of the present method. Therefore, for example, the method may comprise the following steps:.

The second elongate textile body may be a textile body that fulfills at least one of the following conditions:.

The second elongate textile body advantageously has a "reference color" different from the desired color of first elongate textile body.

The reference color may be a parameter that has been entered through an interface into the apparatus, in particular by the user. Advantageously, though, the reference color is measured (i.e. derived from a measurement) by at least one of the sensor heads of the apparatus. This sensor head may be the "first sensor head" mentioned above (i.e. the same sensor head that is later used for measuring the first elongate body), or it may be another sensor head of the apparatus, i.e. a "second sensor head".

In order to compare the defect color against the reference color(s) of the second textile body (or bodies), the method may comprise the following steps:.

Cross-contamination detection is particularly advantageous when it allows to identify one of several different "second textile bodies" as a potential source of cross contamination. Hence, advantageously, for detecting cross contamination, the defect color is compared against the reference colors of several different second elongate textile bodies processed in said apparatus. Based on this comparison, plausible sources of cross contamination are identified among the several different second elongate textile bodies. At least some of the several different second elongate textile bodies advantageously differ in their reference colors.

As mentioned, the invention also relates to a yarn manufacturing apparatus. This apparatus comprises a plurality of sensor heads and a control unit adapted and structured to carry out the method of the present invention.

The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof.

A color parameter comprises at least two color components of a color space, advantageously at least three color components of a color space as measured by a sensor head. The color space is used to describe the color detected by the sensor head. In this context, the color space advantageously spans at least the visible spectrum, e.g. between wavelengths of <NUM> and <NUM>. It may, however, also extend into the ultraviolet, e.g. at least as low as <NUM>, and/or into the near infrared, e.g. at least as high as <NUM>.

For example, the components may describe the optical reflection or transmission of the textile body in at least two different spectral ranges. Advantageously, there may be at least three color components indicative of the optical reflection or transmission of the textile body in at least three different spectral ranges of the spectrum between, in particular with each of the at least three different colors falling at least in part into a spectral range of <NUM> and <NUM>, in particular at least in part into the visible spectral range of <NUM> and <NUM>.

The color space may also be the RGB color space or another color space indicative of the color in three or more different spectral ranges, or it may be the CMY or HSV color space, with the components being at least two of the coordinates in these color spaces.

A textile body may be a yarn precursor, in particular a stream of flocks in the blow room, or a sliver or roving as obtained by carding and drawing, or it may be a yarn as obtained by the spinning process. Advantageously, though, at least the textile body sampled at the claimed "first" sensor head is a yarn.

The yarn manufacturing apparatus comprises at least one of a blow room machinery, a carding machine, a drawing machine, a spinning machine, a winder, and a yarn clearer. "Blow room machinery" advantageously designates an opening machine or a cleaning machine, as they are e.g. found in the blow room.

Storing values permanently is advantageously to be understood as storing said values in non-volatile memory for at least a day.

<FIG> schematically shows some elements of a cotton spinning mill as an example of a yarn manufacturing apparatus.

As known to the skilled person, such a spinning mill comprises a blow room <NUM>, where bales of cotton are opened into flocks, which may then be precleaned as well as fine cleaned. In this process, one or more streams of flocks are generated.

The stream(s) of flocks may be run past sensor heads <NUM>, e.g. in the fine cleaners <NUM> of the blow room <NUM>. The function of these sensor heads <NUM> will be described below.

The mill further comprises a carding and drawing section <NUM>, where the fibers in the flocks are separated, straightened, and formed into slivers and rovings. The slivers or rovings may again be run past sensor heads <NUM>.

In a next step, the products from carding and rowing section <NUM> are fed to a spinning section <NUM>, where they are spun into yarns in a plurality of spinning units <NUM>. The yarns may again be run past sensor heads <NUM>.

Finally, the yarns, e.g. wound on cops, are fed to a winding section <NUM> having a plurality of yarn clearers <NUM>. In each yarn clearer <NUM>, the yarn is run past a sensor head <NUM>, e.g. while being wound from cops onto a bobbin.

The apparatus further comprises a control unit <NUM> controlling the operation of the individual components. In <FIG>, it is depicted as a single element, but it may also be distributed and/or have sub-sections attributed to the different parts of the apparatus.

Typically, control unit <NUM> comprises at least one CPU <NUM>, memory <NUM>, input interfaces <NUM> for receiving sensor signals, e.g. from the various sensor heads as well as from other sensors of the apparatus, output interfaces <NUM> for controlling actuators of the apparatus, at least one input control <NUM> for receiving user input, and at least one display <NUM> for displaying information to the operator.

Control unit <NUM> may perform various functions using program code and parameters stored in memory <NUM>. In particular, it is programmed to carry out the methods as described herein.

As mentioned above, the apparatus comprises a plurality of sensor heads <NUM>, <NUM>, <NUM>, <NUM>. Each of these sensor heads is adapted to measure at least one color parameter Pk of the textile body (which may be a yarn or yarn precursor as mentioned above) being run past it, with k = <NUM>. K being an index of the sensor head and K being the number of sensor heads. The measurements carried out by the sensor heads take place online, i.e. they take place on a textile body while it is being processed by the apparatus.

The values of the color parameters are measured repetitively, at a plurality of different times tik, advantageously at regular time intervals.

Each color parameter comprises several color components C<NUM>. CN of a color space, with N being at least <NUM>, in particular at least <NUM>. Each such color component Cn may describe (i.e. may depend on) the interaction of the textile body with light of a given spectral range Wn, wherein the spectral ranges of the different color components differ from each other.

In one embodiment, the color space may be based on different spectral components in a spectral range as specified above.

Advantageously, each color component Cn describes the optical reflectivity of the textile body in the given spectral range. However, in another embodiment, each color component Cn may describe the optical transmission of the textile body at the given spectral range.

The spectral ranges are advantageously non-overlapping and/or span a substantial part of the visible spectrum. This is illustrated in <FIG>, which shows the normalized sensitivities of the color components Cn with n = <NUM>. N (in this case for N = <NUM>) as a function of the wavelength λ. For each color component, Wn denotes the spectral range or width at half maximum and Mn the wavelength of maximum sensitivity.

In order to be non-overlapping, there should advantageously be at least two, in particular at least three, color components whose spectral ranges Wn have a mutual overlap by less than <NUM>, with the mutual overlap Oij of two spectral ranges Wi and Wj being defined by <MAT> with Wij being the range where Wi and Wj overlap.

Advantageously, at least one spectral range, in particular at least two of them, should have a width Wn smaller than <NUM> for good color selectivity.

In order to span a substantial part of the visible spectrum, there should advantageously be a least two, in particular at least three, color components whose maximum sensitivities Mn are at least <NUM> away from each other.

Advantageously, at least one maximum sensitivity Mn is in the violet, blue, or green spectral range, i.e. below <NUM>, and at least one maximum sensitivity Mn is in the red spectral range, i.e. above <NUM>. In particular, there is advantageously one maximum sensitivity in the blue or violet spectral range below <NUM>, at least one maximum sensitivity in the green or yellow/orange spectral range between <NUM> and <NUM>, and at least one maximum sensitivity in the red spectral range above <NUM>.

<FIG> shows a schematic diagram of a possible embodiment of one sensor head <NUM>. In the shown embodiment, it comprises three light sources 46a, 46b, 46c and a light detector <NUM>. The light sources 46a, 46b, 46c emit light at different spectral ranges, corresponding e.g. to those in <FIG>. Light sensor <NUM> is sensitive at all these spectral ranges. Control circuitry <NUM> is provided for operating the light sources 46a, 46b, 46c to e.g. emit light pulses sequentially and for detecting the response at light sensor <NUM> for each such light pulse.

The light from the light sources 46a, 46b, 46c falls on the textile body <NUM> to be inspected, interacts with the same, and, after this interaction, it is detected by light sensor <NUM>. The interaction is advantageously a reflection, i.e. light sensor <NUM> detects light from the light sources 46a, 46b, 46c that has been reflected from textile body <NUM>.

By attributing the measured light pulses to the individual light sources 46a, 46b, 46c, control circuitry <NUM> is able to measure the color components Cn for the respective spectral ranges Wn.

In yet another embodiment (not shown), three separate light detectors sensitive only in the different spectral ranges Wn may be used, e.g. in combination with a broadband light source.

An example of a suitable sensor head is e.g. described in <CIT>.

Different sensor heads <NUM> may be used for different types of textile bodies, i.e. bodies in different stages of the manufacturing process.

However, since the color components as obtained by the various sensor heads may be compared to each other, as described below, in an advantageous embodiment, at least several of the sensor heads <NUM> are equipped to measure the same color components Cn, i.e. the color components Cn determined by at least several of the sensor heads are the same components of a common color space. In particular, for a color space based on different spectral components, the spectral ranges Wn and the maximum sensitivities Mn of the several sensor heads are the same.

In particular, when the results from different types of yarn bodies in different stages of the yarn manufacturing apparatus are to be compared to each other (e.g. when the measurement on slivers are to be compared to those on yarns or flocks), the sensor heads used for measuring at different types of yarn bodies are advantageously adapted to measure the same color components Cn.

In operation, control unit <NUM> of the apparatus receives the values of the color parameters Pk of the different sensor heads <NUM>, with k being an index designating the respective sensor head as mentioned above. The number K of sensor heads typically being much larger than <NUM>, in particular larger than <NUM>.

Each color parameter Pk comprises the color components Cn determined by the respective sensor head k, or it may comprise different color components that are determined as a function of the color components Cn actually measured by the sensor heads <NUM>, e.g. color components may be translated into a different color space and/or values averaged over several individual measurements may be used.

Control unit <NUM> may store the values of the color parameters Pk in memory <NUM>, together with the times tik to which they are attributed to, in particular at which they were measured. For example, assuming that N = <NUM>, a table of the values of the components C<NUM>(tik), C<NUM>(tik), C<NUM>(tik), and tik may be stored for a given sensor head k in memory <NUM>.

In addition, control unit <NUM> may store, in memory <NUM>, individual operating parameters Om of the apparatus. These may include one or more of the following parameters:.

The values of the operating parameter(s) Om with m = <NUM>. M are determined at different times tim and may again be stored in memory <NUM>.

The operating parameter(s) Om may be determined from control settings applied to the elements of the apparatus, from input data obtained by the operators of the apparatus, and/or from one or more sensors of the apparatus. For example, the diameter and/or capacitance of a yarn can be determined by suitable diameter and/or capacitance sensor heads of the apparatus.

The color parameters and operating parameters can then be processed by control unit <NUM> in various manner in order to derive information about the apparatus, to control its operation, to diagnose problems, and/or to display information.

Examples of such processing are provided in the following.

As mentioned, the values of one or more of the color parameters Pk, or values derived therefrom, are clustered.

This is illustrated with the example of <FIG>, which shows values of the color parameter measured for a given textile body during a run. In this case, three color components Cn were measured, one for a blue spectral range, one for a green spectral range, and one for a red spectral range. Each one of the dots, +-crosses, and x-crosses in <FIG> stands for the color components measured at one time tik, with i = <NUM> to I and I being the sum of the numbers of dots, +-crosses, and x-crosses.

As can be seen, the vast majority of the values (the ones that are shown as dots) are confined to a comparatively compact "main" group, but there are also two distinct "outlier" groups, one of which is denoted by the +-crosses and the other one by the x-crosses.

In the present case, the main group corresponds to color values close to the expected, desired color of the body while the outlier groups indicate specific, distinct defects in the body.

For example, the x-crosses, which are on the "darker" side of the main group, may be indicative of contamination with a dark contaminant while the +- crosses have a hue that is different from the one of the main group and may therefore be indicative of the contamination with a colored contaminant.

Hence, by grouping the values of the color parameter, additional insight into the nature of defects can be obtained, which allows to better control the manufacturing process. Furthermore, the decision-taking about what to do of the detected anomaly, especially if the textile body segment shall be cut out, can be dependent on the group or cluster membership of the anomaly.

The grouping is based on clustering, which is a dynamic process depending on the distribution of the values of the color parameter and may also rely on operating parameter(s) Om, in particular on attributes of the textile body measured by other sensors. For example, the yarn diameter and/or capacitance may also be used for clustering, e.g. in order to group thickened sections of yarn having specific color characteristics and e.g. distinguishing them over (i.e. group them separate from) non-thickened sections having such color characteristics.

Suitable clustering algorithms are known to the skilled person, see e.g. https://en. org/wiki/Cluster_analysis, in particular one based on a centroid model, where each cluster is characterized by a central color. For example, k-means clustering may be used, see e.g. https://en. org/wiki/K-means_clustering, with k e.g. being set to <NUM> (if one primary contaminant is expected) and/or <NUM> (with two primary contaminants being expected).

Alternatively, algorithms using distribution-based clustering may be used, such as the expectation-maximum algorithm, where the colors are modeled with a fixed number of distributions, see e.g. https://en. org/wiki/Expectation%E2%<NUM>%93maximization_algorithm.

In yet another embodiment, density-based clustering may be used, where clusters are defined as areas of higher density than the remainder of the color data set, such as DBSCAN (density-based spatial clustering of applications with noise), see e.g. https://en. org/wiki/DBSCAN.

Advantageously, the grouping can also rely on prior knowledge about existing groups of contaminants and their typical color.

As mentioned, not each measurement of the color parameter Pk is necessarily grouped or clustered. Rather, values may be derived from the individual measurements the color parameter Pk, e.g. by combining several measurements. For example, the mean of the color deviation over a fixed lengths of textile body or over variable-length abnormal sections may be calculated, and these mean values may than be grouped or clustered. Moreover, a prior decision criterion may be used to decide whether a segment is relevant enough to be clustered, dismissing values of other segments.

As mentioned, a particularly advantageous technique relates to the detection of cross contamination, i.e. of a situation where particles of a material that is being processed or has been processed in the apparatus contaminate another material being processed.

Typically, a check for cross contamination is started when one or more potentially defective sections of a "first" elongate textile body is/are detected.

For example, potentially defective sections may be identified from the values of the color parameter, e.g. using clustering as described in the previous section. In particular, an outlier group may be identified as defective sections sharing a same cause.

To check for cross contamination, the color of the potentially defective section, i.e. the "defect color", is compared (directly or indirectly) against at least one "reference color" of a second elongate textile body processed in said apparatus. The value of the reference color can e.g. be retrieved from the values of color parameters or process parameters as stored in memory <NUM>.

For example, if a yarn (corresponding to the first elongate textile body) should be white, but the defect color indicates that it has a reddish tinge, memory <NUM> may be searched for indications that a reddish yarn (corresponding to the second elongate textile body) with a consistent hue has been processed or is being processed in the apparatus. If yes, cross contamination between this reddish yarn and the white yarn is a likely diagnosis, which then allows taking steps to avoid future cross contamination.

For example, if the used color space describes color values in three distinct spectral ranges as shown in <FIG>, a defect color V = (C<NUM>, C<NUM>, C<NUM>) indicative of the potential defect may be derived from the color components in the potential defect or from a "typical color" of a cluster. This "typical color" may, for example, be the cluster's mean color, robust mean color, median color, and/or color of the centroid.

This defect color V can then be compared against reference colors attributed to various "second" textile bodies as stored in memory <NUM>.

For example, memory <NUM> may comprise a list of the reference colors (e.g. average or median colors) Rj with j = <NUM> to J of other textile bodies currently or recently processed in the apparatus.

In order for cross contamination to be most relevant, the reference colors Rj should be different from the desired color of the first textile body. Cross contamination between two yarns of the same color are typically less relevant than cross contamination between two yarns of different color.

The comparison of the defect color V against the reference color(s) Rj may comprise the following steps:.

The deviation T can e.g. be calculated by vector subtraction of the color components, i.e. <MAT>.

For example, in RGB color space, if the yarn should be white (DC = (<NUM>, <NUM>, <NUM>)) and the defect color has a reddish tinge (e.g. V = (<NUM>, <NUM>, <NUM>)), T would be (<NUM>, -<NUM>, -<NUM>).

<NUM>) The deviation T is compared against the reference colors Rj. The mathematical details of this comparison depend on the used color space and the desired error tolerance of the process.

For example, the comparison may comprise the step of determining, for each reference color Rj, the step of calculating a "contaminant color correspondence value" vj and a "contaminant ratio" wj given by <MAT> <MAT> with the first dot · denoting the scalar product of two vectors and with the vertical lines ∥. ∥ denoting an appropriate norm, e.g. the Euclidean norm, that is the length of a vector.

The contaminant color correspondence value vj is typically largest (close to <NUM>) for the second textile body j (or bodies) that is/are likely to have given rise to cross contamination. It represents how well the hue of the color deviation matches a contamination by the textile body j. A contamination resulting in a mixture of fibers of the first elongate textile body and of fibers of the textile body j, the resulting color can be any color mixture in between the colors of the two textile bodies. wj represents the ratio of contaminant fibers, if the color results of such a mixture. Therefore, for a contamination to be plausible, wj must be between <NUM> and <NUM> after accounting for measurement uncertainty. In the example above, where T = (<NUM>, -<NUM>, - <NUM>), and memory <NUM> stores reference colors of a red textile body R<NUM> = (<NUM>, <NUM>, <NUM>) and a green textile body R<NUM> = (<NUM>, <NUM>, <NUM>)), the correspondence value v<NUM> for the red body would be <NUM> while the correspondence value v<NUM> for the green body would be smaller than <NUM>. Furthermore, the contaminant ratio for the red body is <NUM>, which is a possible mixing ratio i.e. the red textile body is a likely candidate for cross contamination.

Hence, using this technique, potential sources of cross contamination can be identified.

Once one or more candidates have been identified, operating personnel of the apparatus may be able to determine when and how cross contamination occurred, and steps can be undertaken to reduce such cross contamination in the future, e.g. by identifying insufficient cleaning procedures of equipment or by spatially or temporarily separating problematic types of textile material.

In a particularly advantageous embodiment, cross-contamination detection is carried out for the yarns in the yarn clearers <NUM> during rewinding of the yarn. The rewinders and yarn clearers are often placed close to each other and cross-contamination, in particular between neighboring yarn clearers, has been found to be likely.

In another particularly advantageous embodiment, cross-contamination detection is carried out for the spinning units <NUM> based on measurements in the yarn clearers <NUM>. Cross-contamination at that point leads to defects that are being spun into the yarn and that are hard to remove from the yarn without removing the contaminated sections.

Hence, advantageously, the first and second elongate textile bodies are being processed in the yarn clearers <NUM> of the apparatus.

In order to assess the likeliness of cross-contamination, it is also advantageous to store the reference colors of the second elongate textile body or bodies together with the physical location where they have been processed in the apparatus. This allows assessing the spatial distance to the first textile body. A small distance is indicative of a higher risk of cross contamination.

Hence, advantageously, the method comprises the step of storing the reference color(s) of the second elongate textile body or bodies together with a location parameter indicative of a physical location where the second elongate textile body or bodies has or have been processed in the apparatus. This location parameter may e.g. be a unique id of a processing station in the apparatus.

Grouping, and in particular, clustering, may not only provide a better understanding of cross contamination processes, but it may also be applied to other purposes.

For example, it can be used in yarn clearers for defining group-dependent processing rules. For example, it may be desired to provide stricter clearing rules, counting rules, or other processing rules for defects arising from foreign plastic materials than for defects arising from foreign organic materials. Foreign organic materials typically fall into a group that is in the brownish-yellow region of the color space while plastic materials can occur anywhere, which makes it possible to distinguish the two.

Hence, in more general terms, the invention may comprise the steps of.

In yet another important technique, parameters may be correlated in order to detect the origin of problems.

In particular, the operating parameter(s) and the color parameter(s) may be correlated by control unit <NUM>, i.e. it is assessed if the color parameter(s) correlates with the other operating parameter(s). This provides insight into how one or more operating parameters affect the color parameter(s).

Correlation techniques are known to the skilled person, see e.g. https://en. org/wiki/Correlation.

Hence, advantageously, the current method comprises the step of determining the correlation between.

For example, let us assume that the values of the (first) color parameter Pk has been recorded at times tik and stored in memory <NUM>.

For example, assuming that N = <NUM>, a table of the values of the components C<NUM>(tik), C<NUM>(tik), C<NUM>(tik), and tik has been stored. Alternatively to explicitly storing the times tik, other parameters may be stored, such as.

In addition, one or more operating parameter(s) Om(t) as described above with m = <NUM>. M has/have been determined at different times tim and their values are again stored in memory <NUM>.

In that case, the correlation between values VPk derived from color parameter Pk and values VOm derived from the operating parameter(s) Om may be calculated.

Advantageously only one of the time series VOm and VPk is stored and the correlation is calculated live while processing the second time serie.

For example, the value VPk may correspond to one of the color components Cn. Advantageously, though, it depends on more than one of the color components Cn in order to use the information embedded in several components. For example, it may be calculated from the absolute values of the deviations of the color component Pk from the desired color DC as described above, i.e. <MAT>.

The VPk(t1) for a given time t1 may also be calculated from several measured color parameters Pk at different times around t1. In addition or alternatively thereto, the value VOm(t2) for a given time t2 may also be calculated from several operating parameters Om at different times around t2. These values may e.g. be determined using rolling averages, interpolation and/or other data combination techniques.

In yet another embodiment, the values VPk(t1) may e.g. be the number of color faults per yarn length or the number of faults classified in a given cluster for a given section of textile body.

The times t1 and t2 for which the values VPk(t1) and VOm(t2) are to be correlated depend on the stage of the production chain the parameters are associated with. For example, if an operating parameter Om of a spinning section <NUM> affects the color parameter Pk of a yarn as measured in a yarn clearer <NUM>, t2 needs to be at a suitable time before t1. Also, it may be necessary to take into account the different processing speeds of the various processing stages of the spinning mill in order to correlate parameters applying to the same section of a textile body.

Hence, generally, the correlation between the value VPk(t1) attributed to a first time t1 and the value VOm(t2) attributed to a second time t2 has to be determined, with t1 = fkm(t2). The function fkm describes how the times tim of the known values of the operating parameter(s) Om(t) are related to the times tik of the measurements of the color parameters.

Typically, the function fkm is as follows: <MAT> with Δt describing the time delay between the stage the operating parameter is assigned to (e.g. spinning) and the stage where the color parameter is measured (e.g. rewinding in the yarn clearer). k is the ratio of the different yarn processing speeds of at the first and second stage. Δt and k are calculated such that the color parameter Pk at a given location on the yarn is correlated with the operating parameter Om when that same location was processed in the first unit.

The correlation between VPk(fkm(t2)) and VOm(t2) describes if the operating parameter Om has an influence on the color parameter Pk.

Hence, in more general terms, the present invention may comprise the step of determining the correlation of at least one value derived from a color parameter at a given first time t1 with at least one value derived from an operating parameter recorded at a second time t2, with the second time being before the first time.

The correlation technique described in the previous section assumes that the time delay between the measuring times of the color parameters and the operating parameters is known, i.e. that the function fkm is known. Sometimes, however, it may not be known which previous process step in the production chain has an impact on a measured color parameter Pk. For example, humidity and/or temperature may have affected any one of several previous manufacturing steps of a yarn for which a color parameter is determined.

In this case, cross-correlation techniques can be used to identify if the color parameter Pk depends on an operating parameter Om at an unknown, previous time, see e.g. https://en. org/wiki/Cross-correlation. Cross-correlation provides additional information over mere correlation since it also allows determining a time delay between the similar traits.

In that case, a value VPk(t) derived from color parameter Pk may be subjected to cross-correlation with a value VOm(t) derived from the operating parameter(s) Om, e.g. by calculating the integral <MAT>.

Hence, in the present system, a yarn is manufactured in a yarn manufacturing apparatus having a plurality of sensor heads <NUM>, <NUM>, <NUM>, <NUM>, <NUM>. At least one variation parameter is determined from the values of the color parameter as a function of time. This variation parameter may e.g. be obtained from clustering or from cross correlation or from the deviation of the color values from a desired color as described above.

The variation parameter can then be used for assessing the process, i.e. for diagnosing possible causes for problems, and/or it can be used to control the process. The process can be controlled directly by control unit <NUM> after it has analyzed the variation parameter, or the control can be based in operator input after the operator has analyzed the variation parameter.

Claim 1:
A method for controlling or assessing a yarn manufacturing process in a yarn manufacturing apparatus having at least a first sensor head (<NUM>, <NUM>, <NUM>, <NUM>; <NUM>), wherein the method comprises the steps of
running an elongate textile body (<NUM>) past the first sensor head (<NUM>, <NUM>, <NUM>, <NUM>; <NUM>),
measuring at a first plurality of times, by means of the first sensor head (<NUM>, <NUM>, <NUM>, <NUM>; <NUM>), a color parameter (Pk) of the textile body (<NUM>),
forming a time-series of values comprising values of the color parameter (Pk) attributed to different times,
using the values of the color parameter (Pk) for controlling or assessing the process,
characterized in that values (VPk) that depend on the color parameter (Pk) are clustered into color clusters.