Method for operating a clothes drying appliance and clothes drying appliance

Abstract

A method is provided for operating a clothes drying appliance, wherein a moisture content of clothes is determined by measuring a current running through the clothes and wherein the determining takes into account a salt content of the moisture. The clothes drying appliance is adapted to perform the method.

Claims

1. A method for operating a clothes drying appliance, comprising: determining a salt content of moisture emanating from the drying clothes; measuring a current running through the clothes; and determining a moisture content of clothes from the measured current by taking into account the determined salt content of the moisture.

2. The method according to claim 1, wherein the moisture content of clothes is determined by determining a gradient of a curve formed from pairs of varieties representing the moisture content over time, and setting at least one target value of a drying cycle based on the gradient.

3. The method according to claim 2, wherein the at least one target value is set by correcting the at least one target value for a pre-determined salt content by adding an offset value, wherein the offset value is based upon a difference of the determined gradient and a gradient corresponding to the pre-determined salt content.

4. The method according to claim 2, wherein the curve is a linear curve.

5. The method of claim 2, wherein the curve is an exponential curve.

6. The method according to claim 2, further comprising storing gradients from multiple measurements as an average gradient, and setting the at least one target value based on the average gradient.

7. The method of claim 2, wherein the at least one target value comprises a target value representative of the moisture content at which a target time for terminating the drying cycle is reached.

8. The method according to claim 1, further comprising: applying an AC voltage signal to the clothes to produce an AC current, measuring the AC current for consecutive samples, generating from the measured AC current for the consecutive samples an envelope signal, generating a series of maximum values from n consecutive samples of the envelope signal, and passing the series of the maximum values through a logarithmic filter to produce a series of filtered values, wherein the filtered values or a curve derived from the filtered values represent the moisture content of the clothes over time.

9. The method according to claim 8, wherein a target value of a drying cycle representative of the moisture content at which a target time for terminating the drying cycle is reached is based on the filtered values.

10. The method according to claim 8, wherein the envelope signal comprises consecutive peak values extracted from the measured AC current over a corresponding sample time.

11. The method according to claim 8, wherein the filter uses a relation comprising y(m)=y(m1)+log(a, x(m)y(m1)), wherein y(m) is an m-th filtered value, y(m1) is a previous filtered value, a IS a parametric log base and x(m) is an m-th maximum value received from the filter.

Description

(1) In the following description which in particular refers to the figures of the attached drawings, a preferred embodiment of the invention is schematically described in greater detail. In the drawing,

(2) FIG. 1 shows a block diagram of a possible implementation of a moisture content detection of a clothes drying appliance;

(3) FIG. 2 shows process steps to determine a moisture content of clothes to be dried in a clothes drying appliance;

(4) FIG. 3 shows a diagram depicting one possible result from the process of FIG. 2; and

(5) FIG. 4 shows a diagram depicting a relationship between a moisture content and time for a variation of a salt content of the water.

(6) FIG. 1 sketches a possible implementation of a moisture content detection of a clothes drying appliance represented by a tumble dryer 1. FIG. 2 shows process steps to detect a moisture content of clothes to be dried in a clothes drying appliance. A possible concrete embodiment is now described referring to both figures.

(7) The tumble dryer 1 comprises a logic in form of a controller 2, e.g. a micro-controller, for controlling operation of the tumble dryer 1, in particular a drying cycle. The controller 2 inter alia controls operation of an AC voltage generator 3.

(8) The voltage generator 3 generates an AC voltage signal (step S1) of a frequency of about 400 Hz. This frequency has the advantage that it is high enough to prevent electrolysis but is low enough to neglect a capacitance of clothes 6. The AC voltage is about 5 Volts which corresponds an operation voltage Vpp of the controller 2 and is thus particularly easy to generate.

(9) On its output side the AC voltage generator 3 is coupled to a DC cut-off means 4 (or DC filter). By the DC cut-off means 4 the AC voltage signal from the AC voltage generator 3 is DC filtered (step S2) to remove any DC portion that could deteriorate the accuracy.

(10) The AC voltage signal may, in particular be a square (or quasi-sine) wave which is particularly suitable for creating a temporarily constant voltage level for easier analysis or interpretation. However, also other waveforms may be used.

(11) The output side of the DC cut-off means 4 is coupled to two electrodes 5 that are part of a current probe and that are located on a lower apex of a bearing shield of the tumble dryer 1. Thus, a DC-filtered AC voltage signal is applied to the clothes 6 by the electrodes 5 (step S3). The electrodes 5 are regularly covered by different clothes 6 (laundry) tumbled within a rotatable drum of the tumble dryer 1. If the clothes 6 cover the electrodes 5, a current flows through the clothes 6 between the electrodes 5 thanks to the water (moisture) contained in the clothes 6. The moister the clothes 6 are the higher is the current. In other words, the carrier signal's AC current is heavily modulated by the laundry's conductance: when the laundry has temporarily good contact with the electrodes 5, the current is high. This current is detected or sensed by the current probe.

(12) The two electrodes 5 are functionally coupled to a current-to-voltage (CV) converter 7 for easier computation. The current probe may be omitted, and the electrodes 5 may directly be connected to the CV converter 7. The CV converter 7 is coupled to a peak detector 8. The peak detector 8 may be implemented in hardware (e.g. in a respective integrated circuit) or in software (e.g. within the controller 2).

(13) The peak detector 8 detects a peak of the current (esp. of the absolute value of the current) over a predetermined period of time, the sample time, for consecutive sample times (step S4). The peak or sample represents the occasion in which humid clothes best cover the electrodes over the sample time. They give a relatively best approximation of the real moisture content within the sample time. Thus, the peak detector 8 detects a string or chain of (local, over the sample time) peaks or samples. This string of peaks forms a respective envelope signal (step S5). The envelope signal is a representative of the spatially temporary conductance of the clothes 6.

(14) The envelope is or the samples or peaks are sampled frequently enough to satisfy the known Nyquist criterion. In other words, the sample time is so short that the Nyquist criterion is satisfied. In particular, the sample frequency may be two times or more than the expected frequency of the laundry or clothes 6 hitting the electrodes 5. This limits a sample error margin.

(15) The peak detector 8 is connected to the controller 2 (e.g. via an analog-to-digital converter (ADC) which may be part of the controller 2) which computes the string of samples. It is a first computational step (step S6) to determine, from the envelope signal, a maximum value of n consecutive samples or peaks with n being a positive number. The determination or extraction of the maximum value achieves that only a best approximation of the real moisture content of the clothes from a group of n peaks is used for further computation for enhanced accuracy.

(16) Over the measurement time, a series of maximum values is generated (step S7) that is passed through a logarithmic filter to give a series of filtered values (step S8). The logarithmic filter converts a basically logarithmic relation between the moisture content and the time into a linear relationship. The linear relationship or straight line is easier to use for determining the occurrence of a certain incident, e.g. determining when a predetermined target moisture content has been reached. Generally, other filters may also be used.

(17) In the shown embodiment the filter uses a relation comprising the relation
y(m)=y(m1)+log(a,x(m)y(m1)),
wherein y(m) is an m-th filtered value, y(m1) is the previous filtered value, a is a parametric log base and x(m) is an m-th maximum value received from the filter. This relation has been found to give a particularly good compromise between easy computation and good accuracy.

(18) The filtered values y(m) (and thus also the string of filtered values y(m)) may be directly used as representative values of the moisture content of the clothes 6 to control a drying cycle of the tumble dryer 1. The filtered values y(m) may also be translated into (physical) values of the moisture content G of the clothes 6, e.g. by using a experimentally of computationally predetermined characteristic curve or relation. For example, the filtered values y(m) may be compared to a target value yend for reaching a target moisture content Gend at the end of a drying cycle, and the drying cycle may be stopped if this target value yend is reached or exceeded.

(19) FIG. 3 shows a diagram depicting one possible string of filtered values y(m) over time t. By having passed through the logarithmic filter, the values y(m) substantially form a straight line or curve C1 that is pointed downward. Each of the values y(m) corresponds to a value G(m) of the moisture content G or moisture content of the clothes. This correspondence can generally described by the function G(m)=f(y(m)) or G(t)=f(y(t)). If y is equal to the target value yend, the target moisture content Gend has been reached. This may be determined by the fact that one of the filtered values y(m) exceeds (i.e., is smaller than) the target value yend and/or by a filtered value y(m) is within a pre-determined margin around the target value yend.

(20) FIG. 4 shows a diagram depicting a relationship between the filtered values y(m or t) and time t or series number m for a variation of a salt content c or salt concentration of the water used for washing the clothes, e.g. tap water. The time t basically correlates to the series number m and can be used in exchange.

(21) Here, the moisture content G (as a physical quantity) can be determined from the filtered values y by the function G=f(y), as described above. The function f may be determined by experiments comparing a moisture content G of the clothes (measured by a different method) with a measured current or quantity derived from it (e.g. the filtered value y). Up to now, the function f is determined without regard for a salt content c.

(22) Curve C1 depicts the linear relationship of the filtered values y and the time t for a medium salt content c, as also shown in FIG. 3. Thus, using water with the medium salt content c gives values of y for which the corresponding moisture content G is known because the function f is correct. However, when the actual salt content c is different from the assumed medium salt content c, the function f introduces an error into determining or knowing the moisture content G. This also holds if the filtered values as such are used to control the drying cycle.

(23) Curve C2 shows a linear relationship of the filtered values y and the time t for a high salt content c. Curve C2 is different from curve C1 in that it is located above curve C1, i.e. that its values y are greater than for the curve C1 for a given point of time. Since the drying process itself (e.g. the energy input) is the same for each curve C1, C2, C3, the real moisture content G at each point of time is also the same. The higher salt content c, however, leads to a higher current and thus to a higher filtered value y and a too high calculated or assumed value of G if using the function f determined for the medium salt content c. Then, a correct target time tend that represents the point of time t to achieve the (real) desired or target moisture content of the clothes would be overstepped. In other words, the assumed target value yend=yend() for the medium salt content would be reached some time t() after the correct target time tend (=t()). This leads to too dry clothes and a waste of energy.

(24) Analogously, curve C3 shows a linear relationship of the filtered values y and the time t for a low salt content c. Curve C3 differs from curve C1 in that it is located below curve C1, i.e. that its values y are smaller than for the curve C1 for a given point of time. This is because a lower salt content c leads to a smaller current, to a smaller filtered value y and thus to a too small calculated or assumed value of G. If a drying process is performed on clothes washed with the low salt content c, the correct target time tend would be achieved too early at a time t(). In other words, the assumed target value yend would be reached some time t() before the correct target time tend. This leads to too moist clothes.

(25) To get or assume correct values of the moisture content G (or a related quantity) for a varying salt content c, a gradient g of the curve or relationship between measured current values or values derived from that, e.g. y(m), is considered. This makes use of the fact that the gradient g=g(c) is the steeper the higher the salt content c is. The salt content c can thus be derived by determining the gradient g(c). In other words, the gradient g(c) is used as a measure of the salt content. The gradient g(c) represents the salt content c at a user's premises. For example, the gradient g(c) can be determined for the curves C1 to C3 by g(c)=y/t. In the present case the gradients g show the following relation: =g(high salt content)>=g(medium salt content)>=g(low salt content).

(26) This gradient g(c) can for example be used to adapt the target value yend to represent the correct target time tend, i.e. yend=yend(g(c)). For example, the target value yend() for the medium salt content can be offset (raised/lowered) to values yend() or yend() if the gradient g(c)= or is steeper and less steep, respectively, than the gradient g(c)= for the medium salt content c. The value of the offset can vary with the difference of the gradient values , , . The offset can be determined experimentally. Different values for the offset relating to different gradients g(c) can be stored in memory, e.g. in a look-up table. Therefore, for example, the drying cycle is terminated at a time yend() if the gradient g(c) of the curve C2 has been determined to be , which gives the correct target time tend.

(27) Also, the gradient g(c) of a drying cycle can be stored in a memory and later retrieved and/or used for the next drying cycle such that the next drying cycle can use corrected values for controlling it from the beginning.

(28) Furthermore, the gradients g(c) of multiple drying cycles can be stored as an average value to make the correction even more robust. Here, it is assumed that the salt content c at the user's premises is at least quasi-constant. The average value may be an arithmetical average or an exponential average. The exponential average may be preferred to give a greater weight to newer gradient values.

(29) Further, the memory may be erased, e.g. if the clothes drying appliance is to be located at another place that may have tap water of a different salt content.

(30) Of course, the invention is not limited to the embodiment as described above.

(31) For example, the gradient is not limited to a gradient from a linear relationship between values representing the moisture content and the time or a corresponding parameter (linear gradient), e.g. a relationship of the filtered values y over time t or series number m. Alternatively, the gradient may be a gradient from a non-linear relationship (including a curve fit) between values representing the moisture content and the time or a corresponding parameter (non-linear gradient), e.g. an exponential relationship of the (unfiltered) maximum values over time t or series number m. The corresponding gradient may be an exponential gradient or exponential parameter characteristic for this relationship.

LIST OF REFERENCE NUMERALS

(32) 1 tumble dryer 2 controller 3 AC voltage generator 4 DC cut-off means 5 electrode 6 clothes 7 current-to-voltage converter 8 peak detector C1 curve of medium salt content C2 curve of high salt content C3 curve of low salt content tend time to terminate drying cycle t() time to terminate drying cycle of curve C2 if yend() is assumed t() time to terminate drying cycle of curve C3 if yend() is assumed y(m) filtered value for series number m yend value of filtered value corresponding to tend yend() value of filtered value corresponding to tend for gradient yend() value of filtered value corresponding to tend for gradient yend() value of filtered value corresponding to tend for gradient gradient of the curve C1 of medium salt content gradient of the curve C2 of high salt content gradient of the curve C3 of low salt content