FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
As amended by the Patents (Amendment) Act, 2005
&
The Patents Rules, 2003
As amended by the Patents (Amendment) Rules, 2016
COMPLETE SPECIFICATION
(See Section 10 and Rule 13)
TITLE OF THE INVENTION: “METHOD FOR OPERATING A TURBIDITY SENSOR IN WASHING MACHINE”
APPLICANT(S)
Applicant : IFB Industries Limited
Nationality : Indian
Address :Verna Industrial Estate, Verna – 403722, Goa
The following specification particularly describes the invention and the manner in which it is to be performed.

METHOD FOR OPERATING A TURBIDITY SENSOR IN WASHING MACHINE
FIELD OF INVENTION
[001] The present disclosure generally relates to a washing machine. More particularly, the present disclosure relates to a method of operating a turbidity sensor in the washing machine to reduce time and detergent for wash, water and power consumption.
BACKGROUND
[002] It is known that a turbidity sensor is used in a washing machine to measure turbidity of the water. The turbidity sensor utilizes light to convey information about turbidity in water. Typically, the turbidity sensor transmits the information or data to a microcontroller unit which process the data and controls the operation of the washing machine. The data received by the turbidity sensor may contain noise due to the turbulent behaviour of the environment in the washing machine in which the sensor is working. The noise may be an electrical noise that is added to the sensor signals due to high turbulence present in the washing machine environment. The electrical noise affects operation of the turbidity sensor and the microcontroller unit.
[003] Further, it is observed that the turbidity sensor has an inherent behaviour in which an output voltage increases when the temperature increases. The increase in the output voltage is attributed to the intrinsic property of turbidity sensing electronics. This is due to the material of the sensor electronics which is semiconductor by nature.. Further, the increase in temperature leads to ambiguous condition in sending accurate signals to the microcontroller unit thereby affecting its decision making capability.
[004] Further, dirt may dissolve in the water due to detergent, mechanical action, temperature and time. Moreover, the entire portion of dirt may be dissolved into the wash liquor or may be the cloth is very clean or may be the clothes are dirty because of sweat. All these conditions are treated as a saturation point. Detecting the saturation point is important to take a decision to stop the wash and move in to next the rinse cycle. The detection of saturation point also assists in decision making to stop the cycle after rinse and spin.

[005] It is know that foam is generated when water is soft or an amount of detergent present is in excess quantity. After generation of the foam, the noise in the sensor signal increases significantly. The noise is generated due to multiple refraction of IR light from emitter and multiple lights received at receiver. Normally, when the foam is less, the foam floats over the water surface and does not interfere with readings of sensor, but increased amount of foam increases water bubble density, which leads to noise.
[006] Typically, the turbidity sensor works in a contaminated environment. The sensor surface is stained due to iron ore in water, minerals, mud, scaling, detergent, water etc. This leads to faulty readings by the sensor. The machine should smartly able to inform that the sensor is stained and also should be able to differentiate staining with respect to bad water quality. The staining of the sensor can also be due to hard water content, due to mold or algae, fungi formation in the washing machine. This leads to reduced washing efficiency and chances of bacterial infection are high. This situation can be solved using the descale agent which is an acidic powder especially designed to dissolve the scales, algae, fungi and/or mold. In addition, the user needs to be informed for cleaning of sensor in case descale does not work.
[007] Therefore, a method is needed that reduces the noise generated by the sensor, provides temperature compensation, detects saturation of wash, detects foam and also intimates the user on using descale and tub clean program or perform the steps of moving the spray part in the sensor housing up and down to clean the sensor by virtue of a cleaning surface connected to the spray head and is in the vicinity of turbidity sensor surface.
SUMMARY
[008] The following summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, example embodiments, and features described above, further aspects, example embodiments, and features will become apparent by reference to the drawings and the following detailed description.
[009] Example embodiments provide a plurality of methods to be implemented in the washing machine which reduces time and detergent for wash, thereby reducing water and power consumption.

[0010] In one aspect of the present disclosure, a method of operating a turbidity sensor in a washing machine is disclosed. The method comprises receiving, by a microcontroller unit, data values associated with noise, detergent type, temperature, saturation of wash, foam, and stains on surface of the turbidity sensor. The method comprises calculating, by the microcontroller unit, a median value for the data values received for noise, detergent type, temperature, saturation of wash, foam, and stains on surface of the turbidity sensor. The method comprises applying, by the microcontroller unit, an exponential smoothening technique on the median values calculated. The method comprises applying, by the microcontroller unit, a temperature compensation technique on the median values calculated. The method comprises comparing, by the microcontroller unit, an average of the data values received for the noise, detergent type, temperature, saturation of wash, foam, and stains on surface of the turbidity sensor with an average of the median values calculated after applying the temperature compensation technique. The method comprises operating, by the microcontroller unit, the turbidity sensor by determining number of rinse cycles required for washing laundry based on the comparison.
[0011] In another aspect of the present disclosure, a plurality of methods is executed to improve performance in operating the turbidity sensor. In order to improve the performance of the turbidity sensor, an apparatus for housing the turbidity sensor is provided. The apparatus is placed on top of a rubber sleeve to have laminar water flow leading to better separation in data between noise and actual signal. Based on the noise and actual signal, several methods are executed to improve performance in operating the turbidity sensor. In one example, a method of data logging and noise reduction is disclosed. The method of using noise of data to differentiate between detergent powder and liquid is disclosed. Further, a method of temperature compensation is disclosed. A method to detect saturation of wash is also disclosed. Moreover, a method of deciding number of rinses to be operated in the washing machine cycle is disclosed. Furthermore, a method of foam detection is disclosed. A method of detecting stains generated on the surface of the sensor is also disclosed. In addition, a method of intimation to the user on using descales and tub clean program is disclosed. Furthermore, in case a descale program does not work, the user assisted cleaning of the sensor is also disclosed.

[0012] Further, a method to increase user satisfaction by giving psychological threshold for washing clothes is disclosed. A method of detection of amount of detergent present in water is also disclosed. In addition, a method to recognise the kind of dirt and amount of dirt in the wash liquor is disclosed. Further, a method of signal processing to obtain data and a method to detect colour bleeding in wash liquor is disclosed. Lastly, a method of water quality measurement and preventive maintenance and user satisfaction is disclosed. This method includes intimation to user that wash quality might get affected due to bad quality of water.
BRIEF DESCRIPTION OF THE FIGURES
[0013] These and other features, aspects, and advantages of the example embodiments will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings.
[0014] FIG. 1A illustrates a washing machine, in accordance with one embodiment of the present disclosure;
[0015] FIG. 1B illustrates a turbidity sensor, in accordance with one embodiment of the present disclosure;
[0016] FIG. 1C illustrates an apparatus for housing the turbidity sensor, in accordance with one embodiment of the present disclosure;
[0017] FIG. 2A and 2B illustrate a graphical representation depicting a comparative analysis of original data against smoothen data obtained before and after the implementation of an algorithm for data logging in a controller present in a washing machine and reduction of noise present in sensor data, in accordance with one embodiment of the present disclosure;
[0018] FIG. 3 illustrates a graphical representation depicting the processed data after implementing the median filter and exponential filter for reducing the noise generated by the sensor, in accordance with one embodiment of the present disclosure;

[0019] FIG. 4A, 4B and 4C illustrate for determining difference between detergent in powder and liquid form, in accordance with one embodiment of the present disclosure;
[0020] FIG. 5 illustrates a graphical representation depicting the detection method using noise data to differentiate between detergent in powder and liquid form, in accordance with one embodiment of the present disclosure;
[0021] FIG. 6 illustrates a graphical representation depicting a comparative analysis of one or more type of detergents plotted with respect to time, in accordance with one embodiment of the present disclosure;
[0022] FIG. 7 illustrates a graphical representation depicting a cut-off, a threshold for differentiating between powder and liquid detergent, in accordance with one embodiment of the present disclosure;
[0023] FIG. 8 illustrates a method for temperature compensation, in accordance with one embodiment of the present disclosure;
[0024] FIG. 9 illustrates a graphical representation depicting the difference plots by scattering plot deviation obtained by implementation of temperature compensation, in accordance with one embodiment of the present disclosure;
[0025] FIG. 10 illustrates a plot of difference between turbidity of clean water and turbidity of soiled medium vs temperature, in accordance with one embodiment of the present disclosure;
[0026] FIG. 11-15 illustrate test cases showing examples of saturation, in accordance with one embodiment of the present disclosure;
[0027] FIG. 16A, 16B, 16C and 16D a method used to calculate number of rinses required, in accordance with one embodiment of the present disclosure;

[0028] FIG. 17 illustrates a tabular representation for the number of rinses to be executed in the washing machine based on the type of detergent used, in accordance with one embodiment of the present disclosure;
[0029] FIG. 18 illustrates a graphical representation generated while applying a method to detect foam, in accordance with one embodiment of the present disclosure;
[0030] FIG. 19A and 19 B illustrate a method for detecting stain, in accordance with one embodiment of the present disclosure;
[0031] FIG. 20 illustrates a graphical representation of the measurements of turbidity measured by stained and unstained sensors, in accordance with one embodiment of the present disclosure;
[0032] FIG. 21 illustrates graphs using a method for detecting stains, in accordance with one embodiment of the present disclosure;
[0033] FIG. 22 and FIG. 23 illustrate the images of the turbidity sensor integrated in the washing machine before and after descaling, in accordance with one embodiment of the present disclosure;
[0034] FIG. 24 and FIG 25 illustrate graphs of the turbidity measured before and after cleaning, in accordance with one embodiment of the present disclosure;
[0035] FIG. 26A and 26B illustrate a method for calculating wash and rinse cycle operation, in accordance with one embodiment of the present disclosure;
[0036] FIG. 27A, 27B, 27C and 27D illustrate a method for calculating turbidity, in accordance with one embodiment of the present disclosure;
[0037] FIG. 28A and 28D illustrate a method for calculating additive rinse, in accordance with one embodiment of the present disclosure;

[0038] FIG. 29 illustrates main function of the washing machine, in accordance with one embodiment of the present disclosure;
[0039] FIG. 30 illustrates an example process for logging data from sensor, in accordance with one embodiment of the present disclosure;
[0040] FIG. 31 illustrates a user interface of the software developed for data logging, in accordance with one embodiment of the present disclosure;
[0041] FIG. 32 illustrates plurality of testing results, in accordance with one embodiment of the present disclosure; and
[0042] FIG. 33A and 33B illustrate sample test for wash quality based on the load capacity and time, in accordance with one embodiment of the present disclosure.
[0043] Further, skilled artisans will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the figures with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.
DETAILED DESCRIPTION
[0044] For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiments illustrated in the figures and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.

[0045] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.
[0046] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises... a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components. Appearances of the phrase “in an embodiment”, “in another embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.
[0048] Embodiments of the present technique disclose a plurality of methods implemented by integration of a turbidity sensor into a washing machine and more particularly to one or more methods that enables the smart working of the washing machine which reduces time and detergent for wash, thereby reducing water and power consumption. The word ‘sensor’ and ‘turbidity sensor’ used in the description reflects same meaning.
[0049] Referring to FIG. 1, a washing machine 10 comprising a turbidity sensor 12, a microcontroller 14 and a memory 16 is shown, in accordance to one embodiment of present disclosure. The turbidity sensor 12 measures the amount of transmitted light to determine the turbidity of the wash water. Specifically, the turbidity sensor 12 operates on the principle that when light is passed through a sample of water, the amount of light transmitted through the sample is dependent on the amount of soil in the water. As the soil level increases, the amount of transmitted light decreases.

[0050] The data is sent to the microcontroller 14 to make decisions on how long to wash in all the cycles. In the present embodiment, the turbidity sensor 12 senses the signals related to the turbidity and provide digital output to the microcontroller 14. The data is logged in memory 16 present in the washing machine 10. The data may be logged at a frequency of about 1 Hz to ‘N’ Hz, depending on capability of the microcontroller 14. The frequency may be increased based on the requirement.
[0051] Now referring to FIG. 1B, a perspective view of a turbidity sensor 12 used for measuring turbidity during wash cycles and optimizing the wash-program sequence is shown, in accordance with an embodiment of the present disclosure. As known, the turbidity sensor 12 comprises a plurality of hollow projections 13 to house a light emitting element (not shown) and a light detecting element (not shown). The hollow projections 13 may act as a sensing surface for the turbidity sensor 10. In one example, the light emitting element may include an Infrared light-emitting diode (IR LED). In one example, the light detecting element may include an infrared photo-resistor. The turbidity sensor 12 detects a turbidity of the wash liquid flowing between the light emitting element and light detecting element. In order to detect the turbidity, the light emitting element emits light which passes through the medium like water and/or air. The light is received at the light detecting element. The amount by which the light gets scattered or reflected by soil particles in the water provides information on the amount of turbidity in the water. Based on the turbidity, the wash cycles in the laundry machine are optimized. Specifically, the turbidity sensor senses and sends signals to a microcontroller (not shown) in a laundry machine. The signals are representative of the degree of discoloration or of the transparency of the wash water as well as signals representative of the number of dirt particles detected in the wash water during the measuring interval. Based on the degree of discoloration and the number of dirt particles, the microcontroller then calculates the degree of turbidity which, in turn, influences the program sequence of the laundry machine.
[0052] For example, the results of detection by the turbidity sensor 12 are used to determine a degree of soil of laundry. Further, the turbidity sensor 12 is used to control a time period of the wash step based on the soil degree determined. As such, the washing operation is controlled on the basis of the results of detection by the turbidity sensor 12.

[0053] Referring to FIG. 1C, an apparatus 20 for housing the turbidity sensor 12 is shown, in accordance with one embodiment of the present disclosure. Specifically, The apparatus 20 comprises a first tube structure 21 and a second tube structure 22. The first tube structure 21 and the second tube structure 22 are coupled in perpendicular. The first tube structure 21 may be provided in a cylindrical structure. The first tube structure 21 comprises a first end (not shown) and a second end (not shown). The first end may comprise a circular opening. Further, the second end may be tapered. The second end may comprise a circular opening. It should be understood that the diameter of the first end is greater than the diameter of the second end. In one embodiment, the apparatus 20 may comprise a plurality of protrusions 25 at the first end. The plurality of protrusions 25 may be used to couple the turbidity sensor 12.
[0054] Referring to FIG. 1C, a cut-section view of the apparatus 20 for housing the turbidity sensor 12 is shown. As can be seen, the first tube structure 21 comprises at least two cut portions 26 provided therein to receive the hollow projections 11 of the turbidity sensor 10.
[0055] Further, the apparatus 20 comprises a tube locker 27 provided at one end of the second tube structure 22 as shown in FIG. 1C. In one embodiment, the tube locker 27 may be coupled to the second tube structure 22 using a snap mechanism. Alternatively, the first tube structure 21, the second tube structure 22 and the tube locker 27 may be provided as a single component. In one embodiment, the tube locker 27 may comprise a rod 28 extended till the length of the cut portions 26 present in the first tube structure 21. In operation, a user of the laundry machine may operate the tube locker 27 e.g., by pushing the tube locker 27 to move the surface of the cut portions 26 through the rod 28 such that the surface of the hollow projections 13 of the turbidity sensor 12 is shaken. When the outer surface of the turbidity sensor 12 is moved, the dirt on the turbidity sensor 12 may fall off thereby cleaning the turbidity sensor 12. In other words, the user may push the tube locker 27 moving up the rod 28 which in turns pushes the hollow projections 13 of the turbidity sensor 12.
[0056] In order to improve the functioning of the turbidity sensor 12, at first, the turbidity sensor 12 is placed within the apparatus 20 by coupling the plurality of hollow projections 13 in the at least two cut portions 26. Subsequently, the apparatus 20 is coupled to a circular rubber frame (not shown) provided on a drum of the washing machine 10.

[0057] The below paragraphs described herein discloses a plurality of methods implemented for operating the turbidity sensor 12 in the washing machine 10. The methods described herein facilitates reduction of noise generated by the sensor, provision of temperature compensation methods, detection of saturation of wash, detection of foam and also intimation methods to the user on using descale and tub clean program. In addition the methods disclosed herein also enables the user to perform the steps of moving the spray part in the sensor housing up and down to clean the turbidity sensor 12 by virtue of a cleaning surface connected to the spray head and is in the vicinity of turbidity sensor surface.
[0058] FIG. 2 is a graphical representation 200 depicting a comparative analysis of original data against smoothen data obtained before and after the implementation of an algorithm for data logging in the microcontroller 14 present in the washing machine 10 and reduction of noise present in sensor data, implemented according to aspects of the present technique.
[0059] In one embodiment, a method for data logging in the microcontroller 14 present in the washing machine 10 and reduction of noise present in the sensor data is disclosed. For example, the noise may be an electrical and/or electronic noise that is added to the sensor signals due to high turbulence present in the washing machine environment. The method of data logging and noise reduction is a continuous activity. In the illustrated embodiment, the data is logged in non-volatile memory 16. The frequency of data logging may be in the range of about 1 Hz to ‘N’ Hz, depending on the microcontroller 14 capability. The frequency may be increased based on the requirement. For example, the data generated by sensor is in milli volts. In the same embodiment, the sensor data inherently is containing noise due to the turbulent behaviour of the environment in the washing machine in which the sensor is working. This noise affects better decision making capability of the microcontroller 14.
[0060] The methods implemented to reduce the noise generated by the sensor are described in detail below.
[0061] In one example, a median filter is applied on the data obtained. Subsequently, the data is arranged in ascending order and the median is chosen as a start value to apply an exponential smoothening technique. The exponential smoothening technique is applied using following formula.

[0062] Value = Median *0.9 + alpha* Current value, where alpha is a value chosen to smoothen data. Here, the value of alpha is based on the responsiveness of filter. FIG. 2A and 2B illustrate example graphical representation 200 depicting a comparative analysis of original data against smoothen data for different values of alpha.
[0063] The exponential smoothening removes sudden spikes and retains the original shape peaks of original data. The methods used are indicative and not restricted and limited to as mentioned. These methods are useful for smaller computing power. For example, as the computing power for washing machine increases, more sophisticated methods of data logging and noise reduction can be implemented. FIG. 3 is a graphical representation 300 depicting the processed data after implementing the median filter and exponential filter for reducing the noise generated by the sensor, implemented according to aspects of the present technique.
[0064] Referring to FIG. 4A, 4B and 4C, a method 400 for determining difference between detergent powder and detergent liquid implemented according to aspects of the present technique is shown. In one embodiment, the method 400 of using noise of unfiltered data to differentiate between detergent powder and liquid is disclosed. In one embodiment, noise is not always bad. In this case, the noise is being used to know which kind of detergent the user has put into washing machine. The method described herein uses an un-filtered and un-smoothened data to take the decision.
[0065] In one example, the data is logged without applying noise filters. From the data, an average value from each window is taken and the average value is compared with a pre-set value. After comparison, if the average value meets the pre-set value, then the decision is taken whether the detergent is in powder form or in liquid form.
[0066] For example, in the method 400 of using noise of data to differentiate between detergent powder and liquid, approximately ten readings are taken and arranged in order ascending order. In one embodiment, the reading is the data logged from the sensor. For example, one reading per second is logged. The minimum value among the arranged readings is saved as lower value. The maximum value among the arranged readings is saved as upper value. The median value among the arranged readings is saved as median value.

[0067] To detect the detergent type, following formula is used and evaluated. D = Square (Lower - median) + Square (Upper - median)
[0068] The value of ‘D’ evaluated is plotted versus time. When the value of D is greater than or equal to about five thousand, the detergent used is liquid detergent else, it can be concluded that the type of detergent used is powder detergent.
[0069] FIG. 5 is a graphical representation depicting the detection method using noise data to differentiate between detergent powder and detergent liquid, implemented according to aspects of the present technique.
[0070] FIG. 6 is a graphical representation 600 depicting a comparative analysis of one or more type of detergents plotted with respect to time, implemented according to aspects of the present technique. FIG. 7 is a graphical representation 700 depicting a cut-off, a threshold for differentiating between powder and liquid detergent, implemented according to aspects of the present technique.
[0071] FIG. 8 illustrates a method 800 for temperature compensation, implemented according to aspects of the present technique. FIG. 9 is a graphical representation 900 depicting the difference plots by scattering plot deviation obtained by implementation of temperature compensation. In this method, it is observed that the sensor has an inherent behaviour in which the output voltage increases as temperature is increased. This is attributed to the intrinsic property of the semiconductors. This leads to ambiguous condition in taking a correct decision.
[0072] The method disclosed herein includes a step to create turbidity ‘T’ which is a constant. Further, the subsequent step is to then increase temperature of the turbid solution and plot a graph of difference versus Time. In one embodiment, the difference is the changed value of turbidity in milli volts (due to temperature) and value of turbidity in volts at about 25 degree Celsius. Further, a line is fitted through the graph and its equation is obtained. For example, the equation obtained is y = 0.1548x - 439.7R² = 0.9935, where x is temperature and y is the adjustment.

[0073] In one embodiment, the temperature of the sensor housing is measured. It should be noted that the temperature sensor inside the turbidity sensor 12 does not have thermal paste applied on to its surface. As the temperature sensor is used to sense the temperature of the apparatus in which the semiconductor electronics is present and accordingly calculate the temperature compensation. For temperature compensation, the compensation is applied for a range of temperature and the range of temperature are increased by step of 5 degree Celsius. This way one fixed compensation is applied for each range of output data of the turbidity sensor.
[0074] This is solved in the current work using a simple technique as described herein. The technique includes application of temperature compensation to the data that is obtained after smoothening. The corrected value is the sensor reading obtained after smoothening plus the adjustment. For example, the adjustment value may be about 0.1548*Temperature - 439.7. The value of adjustment may change as per further developments and is not restricted to the above mentioned method.
[0075] In one example embodiment, another method may include using dark current to compensate the effect of temperature. In this method, the sensor receiver side is not provided with any data from transmitter side. The receiver side readings are taken as temperature increases. It is observed that with increase in temperature the current value is changed. This is logged and is used to compensate the effect of temperature.
[0076] The compensation method includes the steps to create scatter plot of deviation due to temperature versus NTC (Negative temperature coefficient of a temperature sensor). The further step is to plot a regression curve as shown in FIG. 12 and subsequently to verify for different levels of initial turbidity.
[0077] In one embodiment, for better temperature compensation method, a thermal paste present in the sensor is applied to the temperature sensor to bring temperature reading in synchronization with wash liquor. But this way of temperature compensation method was found to be ineffective. To make compensation better, the thermal paste is removed from the temperature sensor and this way it will read the temperature of the environment of up to circuit of turbidity sensor. This way the compensation is better and leads to better results.

[0078] FIG. 10 illustrates a plot of difference between turbidity of clean water and turbidity of soiled medium vs temperature implemented according to aspects of the present technique. The different lines in the graph illustrate plot of difference for different tests conducted.
[0079] FIG. 11 through FIG. 15 illustrates the graphical representations (1100 through 1500) generated while applying a method to detect saturation of wash, implemented according to aspects of the present technique.
[0080] A method to detect saturation of wash is provided. In one example embodiment, dirt may be dissolved into the water due to detergent, mechanical action, temperature and time. In the illustrated embodiment, a threshold point is reached when it would not be possible to remove more dirt. Moreover, the entire portion of dirt may be dissolved into the wash liquor or may be the cloth is very clean or may be the clothes are dirty because of sweat. All these conditions are treated as saturation.
[0081] Detecting this saturation point is important, as based on this point, a decision may be taken to stop the wash and move in to the rinse cycle and conduct spin to end the cloth washing cycle. In one embodiment, a windowed approach may be is implemented. The windowed approach includes the logging the filtered and smoothened data. Further, 2 to N readings per second are taken and for a window of T minutes, the average of logged values is taken. In the illustrated embodiment, a window is a time frame for which reading are taken. For example, each time frame of 3 minutes is considered as window here.
[0082] The below described example illustrates one of a possible way of detecting saturation. The steps carried out for detecting saturation are as described herein. Firstly, average of logged readings is taken for about three minutes. For example, the average number of about 180 readings is taken at this particular step. The average number taken at this step is called as ‘Average 1’. Secondly, the average is saved into a variable. The first and the second mentioned steps are repeated again to obtain another average. The average obtained at this particular step is called as ‘Average 2’. Thirdly, the difference of ‘Average 1’ and ‘Average 2’ is calculated. For example, when the difference calculated is less than or equal to about 80, then the program is repeated for three windows, else the first and second steps are repeated to make ‘Average 1’ equal to ‘Average 2’. Further, after completion of three windows of wash cycle, the rinse cycle is activated and then spin cycle etc. and further steps are conducted. In

one example embodiment, to detect saturation, the calculated ‘Average 1’ and ‘Average 2’ values are normalized.
[0083] It should be understood that the above example is provided for illustration purpose only. In one example, difference of two averages may be stored as a variable e.g., as D1. Further, the above process may be repeated for two windows to obtain another two averages and obtain difference of averages D2. Then D1 is compared with D2. If the value of D1 is less than a present value, then saturation is detected.
[0084] In one example embodiment, the method to detect saturation is to plot the slope of the line through data points of the graphs. When the slope of the plotted line is zero or approaches zero, then saturation is detected. The graphs as shown from FIG. 11 through FIG. 15 represent test cases showing examples of saturation. This can be understood as the plotted line becomes flat or its slope approaches zero. The FIG. 15 shows the concept of windowed approach.
[0085] In the windowed approach, the inventive steps needs to be combined with psychological threshold concept wherein the clothes are clean and the washing needs to be conducted for a minimum amount of time to satisfy the user that clothes are being cleaned, also to tackle the uncertainty in case when the stubborn dirt is not removed from the cloth surface. In addition, when customer or a user selects a hot wash cycle wherein the heater gets on, the sensor readings are not taken till the heating cycle is over.
[0086] FIG. 16A, 16B, 16C and 16D illustrate a method 1600 used to calculate number of rinses required, in accordance with one embodiment of the present disclosure. Further, referring to FIG. 17, a tabular representation 1700 illustrating the number of rinses to be executed in the washing machine based on the type of detergent used, implemented according to aspects of the present technique. The rinses are done on to the clothes to remove detergent. The number of rinses depends on the kind of detergent used. For example, when the detergent is in form of liquid then less rinsing is required. The decision on the number of rinses also depends on the foaming during intermediate spin, the 1st rinse and during wash cycle. Further, the rinsing is also affected by the wash load, kind of cloth, water quality, detergent type and amount of detergent used.

[0087] In the illustrated embodiment, the load of the clothes is identified, kind of cloth is identified, type and amount of detergent is also identified, and water quality is identified with the help of sensor. Such situation requires control like fuzzy. For example, fuzzy logic control known in the art will be used to know the exact number of rinses. The table as illustrated in FIG. 17 provides information on number of rinses to be carried out in the washing machine based on the type and quantity of detergent, amount of load and supplied water quality.
[0088] FIG. 18 illustrates a graphical representation 1800 generated while applying a method to detect foam, implemented according to aspects of the present technique. In one embodiment, a method to detect foam is disclosed. The foam is generated especially when water is soft or the amount of detergent is in excess quantity or no clothes are present or clothes carry detergent from previous cycle. Once foam is generated, the noise in the sensor signal is increased significantly. This noise is due to multiple refraction of IR light from emitter and multiple lights received at receiver. Normally, when the foam is less, the foam floats over the water surface and does not interfere with readings of sensor, but increased amount of foam increases water bubble density, which leads to electrical and/or electronic noise.
[0089] In particular, the foam is detected when the signal variation crosses a pre-set value as compared to average reading. For example, the pre-set value may be in around 800 (difference of sensor reading). When the foam is detected the controller filter outs noise to identify dirt level in the wash liquor. Several filters know in the art may be used to remove noise.
[0090] FIG. 19A and 19B, a method 1900 for detecting stain is shown, in accordance with one embodiment of the present disclosure. Now referring to FIGs. 20-25, graphical representations generated while applying the method 1900 to detect stain on the surface of turbidity sensor are shown.
[0091] Stain type detection using air as an input will give same output irrespective of location of the washing machine.

[0092] In particular, FIG. 20 illustrates the graphical representation 2000 of the measurements of turbidity measured by stained and unstained sensors, implemented according to aspects of the present technique. In one embodiment, a method to detect stain on a surface of turbidity sensor is disclosed. Typically, the sensor works in contaminated environment. The sensor surface is stained due to iron ore in water, minerals, mud, scaling, detergent, water etc. This leads to faulty readings by the sensor. The machine should smartly able to inform that the sensor is stained and also should be able to differentiate staining with respect to bad water quality. For this, air is used to determine the level of staining occurred on the surface of the sensor. Accordingly, the offset value of air is calculated and added to sensor readings to an extent. Subsequently, the user is informed to clean the sensor.
[0093] In particular, FIG. 21 illustrates graphs 2100 generated based on the implementation of the methods for detecting stains as described above. For example, the hard coded value for air may be 2890 milli volts. The offset measured may be of about 2890 milli volts as measured. The offset value gives a measure of level of staining of sensor. The offset value is added to the sensor output reading, till a threshold is reached.
[0094] The graph 2100 shows the reading of sensor focusing in the start of wash cycle. The window shows the sensor reading of the air. Due to staining of sensor over time, the reading of the sensor gets affected. In case of longer run, the threshold can be added or subtracted (depending on the sensor type) to the reading of sensor in air to take care of the staining, but a point reaches when offset does not works and user needs to be informed to clean the sensor or run a descale cycle.
[0095] In particular, FIG. 22 and FIG. 23 illustrate the images of the turbidity sensor integrated in the washing machine before and after descaling. In one example embodiment, the turbidity sensor may be stained because of iron content, silt, sediments, excessive hardness ions, mud present in water. Moreover, the ageing of the turbidity sensor over the time is a challenge in knowing exact turbidity levels. A faulty value leads to wrong decision. To prevent staining of the turbidity sensor, the optimum location of the sensor is the top location which is in the rubber sleeve only wherein the sensor is placed. In particular, FIG. 24 and FIG. 25 illustrate the graphs of the turbidity measured before and after cleaning.

[0096] In one embodiment, methods of intimation on using descale and tub clean program is disclosed. The staining of the sensor can also be due to hard water content, due to mold or algae, fungi formation in the washing machine. This leads to reduced washing efficiency and chances of bacterial infection are high. This situation can be solved using the descale agent which is an acidic powder especially designed to dissolve the scales, algae, fungi and/or mold.
[0097] The user needs to put the powder or liquid to clean the tub, drum or parts in wash liquor enclosure and run tub clean program. Normally this cycle is run at high temperature. A threshold can be set which once crossed, the user will be informed to run descale program.
[0098] In one embodiment, a method to increase user satisfaction by giving psychological threshold for washing clothes is disclosed. In certain situations, the user may even want to wash clothes in washing machine which are clean or does not require much cleaning. The user wants his/her clothes cleaned. In order to tackle such problem, the washing activity should continue for a pre-set time. This is can be understood as Psychological threshold.
[0099] In one example embodiment, the customer psychology may include several mental attributes of the user. For example, the user attributes may include the stopping machine too early because the user may think that machine is malfunctioning. Another attribute may include allowing some time for detergent to dissolve and/or to allow all the clothes to get wet. Another example may include, when there is any heating step then sensing starts only after heater stops. Therefore, there might be delay in sensing for at least 30% of main wash time.
[00100] Referring to FIG. 26A and 26B, a method 2600 for calculating wash and rinse cycle operation is shown, in accordance with one embodiment of the present disclosure.
[00101] Further, referring to FIG. 27A, 27B, 27C and 27D, a method 2700 for calculating turbidity is shown, in accordance with one embodiment of the present disclosure.
[00102] Further, referring to FIG. 28A and 28D, a method 2800 for calculating additive rinse is shown, in accordance with one embodiment of the present disclosure.

[00103] In one example, the data is logged without applying noise filters. From the data, an average value from each window is taken and the average value is compared with a pre-set value. In the current method, since the wash must have had completed and the program might move into rinse stage, foam might be present in the housing or the sensor surface. In order to remove the foam, a two stage recirculation pump on mechanism is used and after that data logging and regular steps used as in detergent type detection are used to detect the additive presence. Subsequently, the data from the detergent type and additive detection is used to control the rinse cycle.
[00104] Further, it should be noted that the additive calculation has to be considered with the calculations of rinse cycle. This is because; if the clothes are washed in a situation where in the detergent type is liquid or less powder detergent is used then additive rinse can be skipped if additive is not present in the washing machine which in turn is known with the help of the algorithm mentioned in the above steps. Further, the rinse can be made better by doing the additive rinse in concentrated form instead of highly diluted form which can be controlled in the cycle by controlling the amount of water used in the additive rinse cycle. This leads to increased efficacy of the additive in the cycle. This is made possible by running the additive rinse in 5 to 7 litres of water instead of 12 to 17 litres of water provided additive presence is detected.
[00105] The additive detection is performed using the two stage recirculation pump by using a method for removing the foam over the sensor surface.
[00106] Further, referring to FIG. 29, a method 2900 illustrating main function of the washing machine is shown, in accordance with one embodiment of the present disclosure.
[00107] FIG. 30 is an illustration of an example process 3000 for logging data from sensor, detecting detergent type, number of rinses and the other methods described above, implemented according to aspects of the present technique. In one example embodiment, an example algorithm for the above mentioned methods is disclosed.
[00108] At step 3002, the data from the sensor is logged. For example, one reading per second is logged. At step 3004, ten readings are arranged in ascending order. In one example embodiment, the minimum value of the ten readings arranged at this step is saved as lower

value. Further, the maximum value of the ten readings arranged at this step is saved as upper value. In addition, the median value of the ten readings arranged at this step is saved as median value.
[00109] At step 3006, the detergent type utilized for washing clothes in washing machine is detected. In one example embodiment, the formula to detect detergent type is: a. D = Square (Lower - median) +Square (Upper - median).
[00110] The value of ‘D’ evaluated is plotted vs time. When the value of D is greater than or equal to about five thousand, the detergent used is liquid detergent else, it can be concluded that the type of detergent used is powder detergent.
[00111] At step 3008, the number of rinse cycles to be carried is determined. In one example embodiment, when the detergent type detected is of liquid type, then the rinse cycle is reduced by one number from the pre-determined number of rinse cycles. The pre¬determined rinse cycles may be based on the type of clothes used for washing clothes.
[00112] At step 3010, the data is again logged from the sensor. For example, one reading per second is logged. At step 3012, a median filter is applied to obtain median value. For example, these applications of median filter works as a start value for the exponential smoothening.
[00113] At step 3014, an exponential smoothening is applied to the data obtained at step 3010. In one example embodiment, a median value as for the start of the readings is selected. In another example embodiment, exponential smoothening is applied. Here the data is smoothened using following formula:
[00114] Value = Median *0.9 + alpha* Current value, where alpha is a value chosen to smoothen data. Here, the value of alpha is based on the responsiveness of filter
[00115] Value = Median *0.9 + 0.1* Current value.
[00116] The value of alpha = 0.1 is taken. The alpha is a weight-age (percentage equivalent).

[00117] At step 3016, temperature compensation is applied to the data obtained after smoothening. For example, the corrected value of data = Sensor Reading (After Smoothening) + Adjustment, where the value of adjustment = .1548*Temperature - 439.7.
[00118] At step 3018, the average of logged readings for about 3 minutes is taken. For example, the average of 180 readings is taken and the value of average is saved into a variable. The saved value is called as ‘Average 1’. The steps 3012 to 3018 are repeated to obtain another average. The average obtained by repeating the steps is saved as ‘Average 2’.
[00119] At step 3020, the Average 1 and Average 2 are compared. At this step, when the difference value is less than or equal to of about 80, then the program is repeated for three windows, else repeat the steps 3012 to 3020 to make ‘Average 1’ equal to ‘Average 2’. In one embodiment, after three cycles of windows are over, rinse is selected.
[00120] At step 3022, the number of rinses is decided. In this embodiment, the rinse cycle is conducted as decided depending on the detergent type. At step 3024, the air reading is compared with hard coded value of air. The comparison is carried out when the variation is more than of about ten percent. At step 3026, the clean water reading is compared with respect to hard coded value. For example, when the variation is more than ten percent, the user is informed to clean the sensor at the end of the cycle. These decisions are made based on a comparison between clean water measurements (taken at the beginning of the wash cycle) and the wash water turbidity measurement taken at the end of each wash cycle. By measuring the turbidity of the wash water, the washing machine can conserve energy on lightly soiled loads by only washing as long as necessary. This will result in energy savings for the user.
[00121] FIG. 31 illustrates the user interface 3100 of the software developed for data logging, implemented according to aspects of the present technique. FIG. 32 illustrates a plurality of testing results 3200 obtained by implemented the methods described herein, implemented according to aspects of the present technique. For example, for a hardcoded wash time which is about 35 mins and algorithm stop point is of about 15 mins, the time saved is 20 mins. It is observed that the average time saving is approximately fifty percent.

[00122] FIG. 33A and 33B illustrate sample test for wash quality based on the load capacity and time, implemented according to aspects of the present technique. In particular, this testing is related with full load and half load of washing clothes. The table 3300-A shows, the effect of integration of turbidity sensor in the washing machine on wash time. The integration of turbidity sensor results the wash time saving by about 50 % for full load. For example, the results for load i.e.; 6 kg clothes for 6 kg capacity washing machine are shown. Table 3300-B shows the effect of integration of turbidity sensor in the washing machine on wash quality. For example, the wash quality remains same for half load. It is observed, that the wash time saving is about 50 % for half load i.e., for 3 kg clothes for 6 kg capacity washing machine. This is achieved when the wash quality is same at perceivable levels.
[00123] It should be noted that use of the exponential smoothening technique, de-noising techniques and data logging techniques are used in conjunction with housing the turbidity sensor in the apparatus which promotes laminar flow and make it easy for separation of noise with actual data. Further, sensor staining error generation and rectification with user action either by using descale like product or by push mechanism of the spay part (tube locker) of the apparatus.
[00124] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.
[00125] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible.

WE CLAIM:
1. A method of operating a turbidity sensor in a washing machine, the method
comprising:
receiving, by a microcontroller unit, data values associated with noise, detergent type,
temperature, saturation of wash, foam, additive detection, and stains on surface of the
turbidity sensor;
calculating, by the microcontroller unit, a median value for the data values received for noise,
detergent type, temperature, saturation of wash, foam, additive detection, and stains on
surface of the turbidity sensor;
applying, by the microcontroller unit, an exponential smoothening technique on the median
values calculated;
applying, by the microcontroller unit, a temperature compensation technique on the median
values calculated;
comparing, by the microcontroller unit, an average of the data values received for the noise,
detergent type, temperature, saturation of wash, foam, and stains on surface of the turbidity
sensor with an average of the median values calculated after applying the temperature
compensation technique; and
operating, by the microcontroller unit, the turbidity sensor by determining number of rinse
cycles required for washing laundry based on the comparison.
2. The method as claimed in claim 1, wherein the noise in data is removed using a median filter.
3. The method as claimed in claim 1, wherein the exponential smoothening technique is used to remove spikes in the data values received from the noise, detergent type, temperature, saturation of wash, foam, and stains on surface of the turbidity sensor.
4. The method as claimed in claim 1, further comprises determining a difference of detergent type between a detergent powder and a detergent liquid.
5. The method as claimed in claim 1, wherein a thermal paste is used to obtain readings related to temperature for applying the temperature compensation technique.

6. The method as claimed in claim 1, wherein the number of rinse cycles required is determined based on the detergent type and additive detection,.
7. The method as claimed in claim 1, wherein air is used to determine level of stain occurred on the surface of the turbidity sensor.
8. The method as claimed in claim 1, further comprises calculating turbidity of the water to operate the turbidity sensor.
9. The method as claimed in claim 1, further comprises calculating additive rinse of the water to operate the turbidity sensor.

METHOD FOR OPERATING A TURBIDITY SENSOR IN WASHING MACHINE
ABSTRACT
A method of operating a turbidity sensor in a washing machine is disclosed. The method comprises receiving, by a microcontroller unit, data values associated with noise, detergent type, temperature, saturation of wash, foam, and stains on surface of the turbidity sensor; calculating a median value for the data values received for noise, detergent type, temperature, saturation of wash, foam, additive detection, and stains on surface of the turbidity sensor; applying an exponential smoothening technique on the median values calculated; applying a temperature compensation technique on the median values calculated; comparing an average of the data values received for the noise, detergent type, temperature, saturation of wash, foam, additive detection, and stains on surface of the turbidity sensor with an average of the median values calculated after applying the temperature compensation technique; and operating the turbidity sensor by determining number of rinse cycles required for washing laundry based on the comparison.