GB2524033A

GB2524033A - Determination of a state of operation of a domestic appliance

Info

Publication number: GB2524033A
Application number: GB1404313.7A
Authority: GB
Inventors: Richard John Bryce
Original assignee: British Gas Trading Ltd
Current assignee: British Gas Trading Ltd
Priority date: 2014-03-11
Filing date: 2014-03-11
Publication date: 2015-09-16
Anticipated expiration: 2034-03-11
Also published as: GB2524033B; GB201404313D0

Abstract

A method of determining a state of operation of a networked domestic appliance in a plurality of domestic appliances using remote monitoring, comprising: receiving S10, from the domestic appliance, a time series of data relating to the operation of the domestic appliance over a cycle of operation; and determining S20 the state of operation of the domestic appliance based on comparing the received time series with a model of time series of data corresponding to the operation of the plurality of domestic appliances over a cycle of operation. A course of action is also determined S30 such as diagnosing a fault, predicting a maintenance need based on a trend of operation, identifying normal wear. The domestic appliance may be a washing machine or boiler for a domestic heating system, for example. The time series of data may relate to a particular component of the domestic appliance over a cycle of operation. The remote monitoring may be performed by a server connected to the plurality of networked domestic appliances.

Description

Determination of a state of operation of a domestic appliance This invention relates, but is not limited, to a method, a device, a computer program product and apparatus for determining a state of operation of one or more appliances, such as domestic appliances.

It is known to monitor remotely the operation of one or more heating systems, such as boilers. In some known examples, a device monitoring the operation of the heating systems triggers alerts in response to fault codes, or crossing of a threshold of a given parameter. In other known examples, the monitoring device reacts to a rate of change of different parameters.

However in the known examples the trigger of the alerts is instantaneous for each monitored parameter. Therefore in the known examples each parameter is taken simply independently, and in-depth analysis of the operation of the heating system or of the cause for the fault is difficult.

Furthermore in the known examples the trigger of the alerts only depends on the level set for the triggering threshold or the triggering rate of change of the parameter. Therefore, even if the operation of the heating system slowly but surely tends to a fault, it is difficult in the known examples to plan a pre-emptive maintenance until the triggering threshold or rate of change is reached. Simply lowering the level of the triggering threshold or triggering rate of change does not solve the problem, as it might generate false alarms and thus unnecessary and costly maintenance.

Embodiments of the present invention will now be described, by way of example, with reference to the accompanying drawings, in which: Figure 1 schematically illustrates a plurality of appliances connected to an example device according to the disclosure, via a communications network; Figure 2 schematically illustrates an example appliance comprising one or more components; Figure 3 schematically illustrates an example boiler comprising one or more components; Figure 4 shows a flow chart illustrating an example method for determining a state of operation of a heating system according to the disclosure; Figure 5 shows a flow chart illustrating an example detail of a method for determining a state of operation of a heating system according to the disclosure; Figure 6 shows a flow chart illustrating another example detail of a method for determining a state of operation of a heating system according to the disclosure; Figure 7 shows an example of a plurality of time series of data relating to the operation of a heating system over a cycle of operation; Figure 8 shows an example of a plurality of model time series of data relating to the normal operation of a heating system over a cycle of operation; Figure 9 shows an example of a plurality of model time series of data relating to the heating operation of the heating system with a blocked condensate drain, over a cycle of operation; and Figure 10 shows an example of a plurality of model time series of data relating to a heating operation of the heating system with a blocked flue intake, over a cycle of operation.

With reference to the drawings in general, it will be appreciated that similar features or elements bear identical reference signs. It will also be appreciated that the Figures are not to scale and that for example relative dimensions may have been altered in the interest of clarity in the drawings. Also any functional block diagrams are intended simply to show the functionality that exists within the appliance (such as a washing machine and/or a heating system as non-limiting examples) and/or the network and/or the appliance, and should not be taken to imply that each block shown in the functional block diagram is necessarily a discrete or separate entity. The functionality provided by a block may be discrete or may be dispersed throughout the device and/or the appliance and/or network, or throughout a part of the device and/or the appliance and/or network. In addition, the functionality may incorporate, where appropriate, hard-wired elements, software elements or firmware elements or any combination of these.

The disclosure relates to the determination of the state of operation of an appliance, such as a domestic appliance, such as a washing machine and/or a fluid heating system, such as a boiler. The determined state may comprise the general condition of the appliance (such as normal wear of the appliance and/or of one of its components) and/or may be for example a normal mode of operation or a faulty mode of operation, such as a heating operation with a blocked condensate drain and/or a heating operation with a blocked flue intake. The appliance may be part of a plurality of domestic appliances connected to a server via a communications network. The server may receive data from one or more sensors and/or one or more components of, or associated to, one of the domestic appliances, the data relating to a cycle of operation. The determination of the state of operation of the domestic appliance may be based on the comparison, for example using pattern matching, of the received data with one or more models of data. The one or more models of data may correspond to different conditions of operation of the plurality of domestic appliances. The model may be assembled from data received from the plurality of domestic appliances or from predefined historic model data.

In the disclosure, a device may monitor the operation of one or more appliances and/or monitor the operation of one or more components of the appliances, over at least a cycle of operation. Thus in the disclosure the trigger of the alerts is not instantaneous for each monitored parameter, contrary to what often happens in the prior art. Therefore in the disclosure, several parameters may be taken independently or together over a cycle of operation, e.g. over a full cycle of operation of the appliance, and in-depth analysis of the operation of the appliance or of the cause for the fault may be facilitated.

Furthermore in the disclosure the monitoring may be performed over more than one cycle. Therefore if it is detected that the operation of the appliance slowly but surely tends to a fault, planning of a timely pre-emptive maintenance (such as repair and/or replacement of the appliance and/or one of its components) may be facilitated.

The example illustrated in Figure 1 shows that a device 10 configured to implement the method of the disclosure may comprise at least a processor 11, a memory 12 and a controller 13.

In the example shown in Figure 1, the device 10 may be configured to determine the state of operation of one or more appliances 2 remotely. The one or more appliances 2 may thus be connected to the device 10 by a communications network 3, and the device 10 may be configured to receive data S from the one or more appliances 2 over the network 3.

In order to receive the data S from the one or more appliances 2 over the network 3, the device 10 may comprise a communications server 1 connected to the network 3. Alternatively or additionally, as shown in Figure 1, the device 10 may be configured to determine the state of operation of one or more appliances 2 at least partly locally, and the device 10 may thus be at least partly located in one or more of the appliances 2.

It is appreciated that the appliance may be any type of appliance, such as a domestic appliance (for example a washing machine or a fluid heating system as non-limiting examples).

The present specification will now be mainly directed to an appliance comprising a domestic fluid heating system which may be any type of domestic fluid heating appliance, which may for example be coupled to a fluid circulation circuit adapted to circulate heated fluid through a heating system of a building and/or to circulate heated fluid directly and/or directly in a domestic space. The heated fluid may be a liquid or gas, such as water or air as non-limiting examples. The domestic fluid heating system 2 may thus define a domestic space and water heating system. As already stated it is however appreciated that the present specification applies to any type of appliance, such as a domestic appliance.

As illustrated in Figure 3, the disclosure advantageously relates, but is not limited, to a domestic boiler 2.

Referring to Figure 3, as a non-limiting example, the boiler 2 may conventionally comprise a housing 202 forming a combustion chamber incorporating a bumer 203 and a heat exchanger 204 having an inlet 205 and an outlet 206 for water to be heated in the heat exchanger 204. The outlet 206 may be coupled to a fluid circulation circuit adapted to circulate heated fluid through a heating system of a building. The housing 202 comprises an exhaust outlet 207 forthe exhaustion of flue gases produced by the burner 203 when heating water is flowing through the heat exchanger 204. The housing 202 also comprises a condensate drain 218 which allows the condensed water vapour produced during combustion to drain away.

Secured to one side 208 of the housing 202 is a fan 209, driven by a motor, which supplies air to the housing 202 through an inlet port 210 in the side 2DB of the housing 202. The burner 203 has an inlet 211 through which gas for combustion reaches the burner 203. The gas is supplied from a constant pressure source and reaches the inlet 211 by way of a valve controlled by a control unit 213. A pilot S gas burner 214 extends from the unit 213 for igniting the gas burner 203.

The unit 213 is itself controlled by a control unit 215, configured to set a power output of the boiler 2.

The unit 215 supplies power to an H.T. generator 216 for generating high voltage sparks to ignite the pilot burner 214. Furthermore the unit 215 is responsive to signals from a flame failure device 217 which senses the presence of the flame of the pilot 214 and the flame of the main burner 203. The unit 215 may also receive data from, and send control signals to, the fan 209.

Figure 1 schematically shows an exemplary device 10 configured to implement a method 100 for determining a state of operation of an appliance, such as a domestic fluid heating system 2, in a plurality of appliances, such as domestic fluid heating systems 2, as schematically illustrated in Figures4to6.

With reference to Figure 4 and in a non-limiting example of a domestic fluid heating system 2, the method 100 mainly comprises: receiving, at SlO, from one of the domestic fluid heating systems 2, a time series of data S relating to the operation of the heating system 2 over a cycle 4 of operation; and determining, at 820, the state of operation of the heating system 2 which sent the time series of data 5, based on comparing the received time series of data 5 with a model of time series of data corresponding to the operation of the heating system 2 over a cycle 4 of operation.

As explained if further detail below, the model of time series of data 50 may correspond to the operation of the plurality of domestic fluid heating systems 2 over a cycle of operation.

Data may also be received from the plurality of domestic fluid heating systems 2, e.g. for assembling the models and/or for statistics purposes, for example for better understanding and/or management of the network of systems 2 by an operator of the network of systems 2.

In some examples and as described in greater detail further below, the method 100 also comprises determining, at S30, a course of action based on the determining at S20. The course of action may comprise: diagnosing a normal or a faulty operation of the heating system 2, predicting a need for maintenance based on the diagnosing (for example on detecting wear of one of the components of the system), and/or triggering maintenance of the heating system 2 based on the predicting.

The disclosure may thus enable the operator of the network of systems 2 to have a better understanding of the operation of the systems and/or to better manage the network.

As described in further detail below, in some examples the diagnosing may further comprise: processing the complex and detailed time series of data 5, and displaying the processed time series of data 5 to a user in a way, for example which can be quickly and/or easily understood and/or navigated.

In the example illustrated in Figure 1, the processed time series of data S may be displayed in an interface which may be displayed on a display screen of a device 70, to be used by a user of the processed time series of data 5. In some examples, the device 70 may be a desktop computer, a laptop computer, a mobile phone, a smart phone, an electronic personal digital assistant, and/or a mobile and portable dedicated handset, etc. In the example illustrated in Figure 1, the processed time series of data 5 to be displayed may be provided to the device 70 over the communication network 3.

Typical users of the processed time series of data S may include: service engineers visiting a user of the system 2 to carry out maintenance (such as check-up, repair and/or replacement), and/or call centre operatives discussing a problem with a user of the system 2, for example over the phone.

Table 1 below illustrates a non-limiting example of an interface on which the processed time series of data 5 may be displayed.

Date/Start Time End time Cycle type Status 02/02/2014 10:10 02/02/2014 10:15 Heating cycle Normal 02/02/2014 10:21 02/02/2014 10:25 Hot water cycle Normal 02/02/2014 10:32 02/02/2014 10:35 Heating cycle Normal 02/02/2014 10:43 02/02/2014 10:50 Heating cycle Abnormal temperature 02/02/2014 10:54 02/02/2014 10:59 Heating cycle Abnormal temperature 02/02/2014 11:05 02/02/2014 11:10 Hotwatercycle Normal 02/02/2014 11:16 02/02/2014 11:20 Heating cycle Overheat error 02/02/2014 11:27 02/02/2014 11:30 Hot water cycle Normal

Table 1

It is appreciated that the processed time series of data may also be organised and/or displayed in a number of alternative ways compared to the example shown in Table 1, including as non-limiting examples horizontal timeline with more graphical indication of the appliance history and state. In

some examples:

the normal status may be further indicated by a specific colour on the interface (such as green), and/or the faulty but not so critical status (such as the abnormal temperature) may be further indicated by a specific colour on the interface (such as yellow or orange), and/or the faulty and critical and/or serious status (such as overheat error) may be further indicated by a specific colour on the interface (such as red).

In some examples, the interface may further be configured to allow a user to navigate down to a display of a more detailed processed time series, from any one of the above-identified cycles, e.g., by performing a clicking or a selection operation. Alternatively or additionally, the interface may further be configured to, for example, allow a user of the processed time series to have a dialog with a user S of the system 2 around, for example, when the appliance was operating normally or not at all. Such a dialog may allow diagnosing simple problems of the system 2 (such as incorrect seftings on a heating controller) which could be directly rectified by the user of the system 2 without the operator of the network sending a service engineer on site.

In some examples, as an entry point to the cycle sets on the interface, the user of the processed time series of data could also be provided with an overview summary of the system state. In the example illustrated in Table 2, the interface may display a few simple indicators of the system history, used for example to quickly ascertain whether any problems were detected and their general nature.

Cycle type Count Normal heating 57 Normal hot water 73 Abnormal temperature 2 Overheat fault 1

Table 2

Alternatively or additionally, the interface may display rolling up' periods of normal operation into a single visual identifier to highlight the abnormal cycles.

As described in further detail below, the triggering of the maintenance may comprise outputting a maintenance instruction and/or actually maintaining the heating system 2.

As illustrated in Figure 2, the device 10 may be configured to define the appliance such as the heating system 2 in terms of a set of components, each having one or more modes of operation. For example the device 10 may define the heating system 2 as comprising one or more components referred to as 21,22, 23 and/or 24, corresponding to e.g., at least one inlet 21 (such as fluid inlets 21), and/or at least one outlet 24 (such as fluid outlets 24); and/or at least one controller 22 (such as a switch on/off controller 22); and/or at least one controller 23 (such as an output power controller 23).

The device 10 may thus be configured to receive the one or more time series of data 5 from one or more of the components 21, 22, 23 and/or 24. This enables in-depth analysis of the operation of the heating system or of the cause for a fault. For example, the data 5 may include a set of time-stamped data: internal data, such as data coming from sensors internal to the system 2, from control state knowledge and/or from operational counters; and/or external data, such as data coming from retrofit sensors and/or from controls information.

The fluid heating system 2 may comprise a controller 25 configured to receive data from sensors and/or from components 21 22, 23, or 24 of the domestic fluid heating system 2; and send data 5 to the monitoring device 10, e.g., over the network 3. The controller 25 may be a built-in component of the system 2 or may be an add-on controller which can be retrofitted on existing systems 2, thus providing data processing and communications functionality to the system 2.

The device 10 may be configured to parse the data S from the system 2 and/or the one or more components 21, 22, 23 and/or 24 once the data S is received, and allot the data to corresponding patterns, as explained in greater detail below.

The device 10 may further be configured to assemble one or more models of time series of data 50 from a plurality of data 5 received from the one or more systems 2, for example during a phase of setting up of the device 10. In otherwords, the device 10 may further be configured to assemble the one or more models of time series of data based on time series of data 5 relating to the operation of the plurality of domestic fluid heating systems 2, received from the plurality of domestic fluid heating systems 2.

Additionally or alternatively, the device 10 may be configured to accept one or more predefined models of time series of data 50, based on programming of the processor 11 of the device 10 by the operator of the network of systems 2 and/or administrator of the device 10 and/or the systems 2. In other words, the device 10 (for example the memory 12) and/or a database 6 may further be configured to store a predetermined model of time series of data.

In either case, the one or more models of time series of data 50 corresponds to patterns of modes of operation (including normal and/or faulty operation) of the system 2 and/or of the one or more components 21, 22, 23 and/or 24. The patterns are determined, e.g., via tests, theory, and/or experience from data sets in different conditions. The different conditions may comprise a normal heating condition, a heating operation of the heating system with a blocked condensate drain and/or a heating operation of the heating system with a blocked flue intake.

It is appreciated that the plurality of domestic fluid heating systems may comprise different types of domestic fluid heating systems. The model of time series of data may thus correspond to a type of domestic fluid heating systems 2. The device 10 may thus be further configured to identify the type of the domestic fluid heating system 2 in the plurality of domestic fluid heating systems 2, based on the received time series of data 5 and/or on identification received from the domestic fluid heating system 2.

The one or more models of time series of data 50 may be determined for each type of system 2 of interest, for example for different types of boilers 2 connected to the device 10 via the network 3. It is appreciated that the models of time series of data 50 may comprise at least a model for each of at least a normal heating operation, a heating operation with a blocked condensate drain, and a heating operation with a blocked flue intake.

As already mentioned, once determined, the one or more models of time series of data 50 are stored in a database 6. As illustrated in Figure 1, in an example the database 6 may be at least partly located in the device 10. Alternatively or additionally, as shown in Figure 1, the database 6 is at least partly external to the device 10 and is connected to the network 3, and the device 10 may be configured to access the database 6 over the network 3. The device 10 may be configured to retrieve the one or more models of time series of data 50 from the database 6 when determining the state of operation of the heating system.

In order to determine the state of operation of the heating system 2, the device 10 may further be configured to compare the received time series of data 5 with one or more models of time series of data 50. The device 10 may be configured to compare the received time series with the models of time series by matching a pattern of the received time series of data 5 (as shown in the key to Figure 7) to a pattern ofa model of time series of data 50 (as shown bythe solid lines in Figure 7).

As already stated, the device 10 may further be configured to determine a course of action based on the determining, and may thus be configured to trigger maintenance of the heating system 2 based on predicting a need for maintenance. The device 10 may thus be configured to timely output a maintenance instruction and/or to perform the maintenance of the heating system 2. The maintenance instruction may be sent, for example, to at least one controller such as the controller 23, locally on the system 2 and/or over the network 3. The maintenance instruction may also be sent to a user of the system 2, for example, via a SMS and/or an email. Some of the maintenance operation may thus be performed directly by the user of the system (for example deblocking a block condensate drain and/or repressurizing the system where a loss of pressure is detected), thus saving costs for the operator of the network of systems. As explained in greater detail below, the device 10 may further be configured to predict longer term trends of operation of the system 2, and may thus lengthen the period of time between successive servicing operations in the network of systems, thus saving costs for the operator of the network of systems.

In reference to Figures 1 and 2: the fluid inlets 21 of the boiler 2 may comprise the inlet 205, the inlet port 210, the fan 209, and/or the inlet 211; and/or the fluid outlets 24 of the boiler 2 may comprise the outlet 206, the condensate drain 218, and/or the exhaust outlet 207; and/or the switch on/off controller 22 may comprise the unit 213 and/or the flame failure device 217; and/or the output power controller 23 may comprise the unit 215.

Figure 7 schematically illustrates that the time series of data 5 may be at least one time series of data 5, i.e. the data 5 may comprise one or more time series of data 5, referred to, e.g. as 51, 52, 53, or 54 in Figure 7.

As illustrated in Figure 7: time series 51 may refer to the heating mode of the heating system 2, for example the on/off state of a flame of a burner of the heating system 2; time series 52 may refer to the temperature of the flow at the output of the heating system 2, for example the temperature of water at an outlet after a heat exchanger of the heating system 2 (sometimes referred to as primary flow); time series 53 may refer to an output power of the heating system 2; and/or time series 54 may refer to the mode of an inlet pump of the heating system 2.

It is thus appreciated that each of the time series 51, 52, 53, or 54 may be the output of a different component of the system 2, for example, in reference to Figures 2 and 3: time series 51 may be the output of the switch on/off controller 22, and in some examples the output from the unit 213 and/or the flame failure device 217; and/or time series 52 may be the output of the fluid outlet 24, and in some examples the output from the outlet 206; and/or time series 53 may be the output of the output power controller 23, and in some examples the output from the unit 215; and/or time series 54 may be the output of a fluid inlet 21 of the system 2, and in some examples the output from the fan 209.

The device 10 illustrated in Figure 1 and the method 100 illustrated in Figure 4 may both take advantage of the fact that: there exists a model for a normal heating operation of the system 2, and that there are usually common ways of failure of a heating system which can be recorded over at least one cycle of operation.

It is thus appreciated that the state of operation of the heating system 2 (including normal operation or faulty operation of the heating system 2) may thus be determined, at 320, based on comparing: time series of data 5 relating to the operation of the heating system 2 over a cycle 4 of operation, with a model of time series of data 50 corresponding to the operation of the heating system 2 (including normal operation or faulty operation of the heating system 2) over a cycle 4 of operation.

-10 -Figures 8 to 10 schematically illustrate that the model time series of data 50 may be at least one model time series of data 50, i.e. the data 50 may comprise one or more models of lime series of data 50, referred to, e.g. as S models 151, 152, 153 or 154 illustrated in Figure 8, corresponding to models for a normal heating operation of the heating system 2, as models 251, 252, 253, 254 or 255 illustrated in Figure 9, e.g., corresponding to models for a heating operation of the heating system 2 with a blocked condensate drain, and as models 351 352, 353, 354 or 355 illustrated in Figure 10, e.g., corresponding to models for a heating operation of the heating system 2 with a blocked flue intake.

As illustrated in Figure 8, for a normal heating operation of the heating system 2: model time series 151 may refer to the heating mode of the heating system 2; model time series 152 may refer to the temperature of the flow at the output of the heating system2; time series 153 may refer to an output power of the heating system 2; andlor time series 154 may refer to the mode of an inlet pump of the heating system 2.

The one or more models for normal heating operation will now be explained with reference to Figure 8. A heating demand by a user activates, at time to, an operation cycle corresponding to a heating operation cycle 4 of the heating system 2. As shown by model 151, after a short period 41 of transient mode of operation, the heating mode of the heating system moves rapidly to a period 42 of steady mode of operation, corresponding to the flame of a burner being active (shown by value 70). As shown by model 153, the output power of the heating system 2 shows a period 41 of transient mode of operation, including gradual increase, to bring the temperature of the flow at the output of the heating system 2 to the appropriate temperature, and then drops (after about 1500 seconds) to a period 42 of steady mode of operation, around 40%. As shown by model 152, after about 2000 seconds, the temperature of the output flow shows a period 42 of steady mode of operation, where it is held at around 88°C. Heating demand is removed by a user at te (around 6500 seconds) and the system 2 returns to quiescent state after a short period 43 of transient mode of operation.

As illustrated in Figure 9, for a heating operation of the heating system 2 with a blocked condensate drain: model time series 251 may refer to the heating mode of the heating system 2; model time series 252 may refer to the temperature of the flow at the output of the heating system 2 (primary flow); model time series 253 may refer to an output power of the heating system 2; model time series 254 may refer to the mode of an inlet pump of the heating system 2; and/or -11 -model time series 255 may refer to the temperature of the flow of domestic hot water (DH (secondary flow).

The one or more models for a heating operation with a blocked condensate drain will now be explained with reference to Figure 9. Heating demand is applied by a user at tO and, as shown by model 252, after a period 41 of transient mode of operation (around 300 seconds) the output temperature reaches a period 42 of steady mode of operation, around 70°C. The condensate drain is blocked, and the combustion chamber of the heating system 2 gradually fills with water, thus reducing the actual heat output of the system 2. Therefore, as shown by model 253, the power of the system 2 gradually ramps up to a maximum (e.g., 100), after which, as shown by model 252, output temperature starts (around 1300 seconds) to drop below target (i.e. 70°C). As shown by model 251, eventually flame extinguishes at tf through oxygen starvation, and the system 2 attempts at about 2200 seconds re-ignition of the flame.

As illustrated in Figure 10, for a heating operation of the heating system 2 with a blocked flue intake: model time series 351 may refer to the heating mode of the heating system 2; model time series 352 may refer to the temperature of the flow at the output of the heating system 2 (primary flow); model time series 353 may refer to an output power of the heating system 2; model time series 354 may refer to the mode of an inlet pump of the heating system 2; and/or model time series 355 may refer to the temperature of the flow of domestic hot water (DHW) (secondary flow).

The one or more models for a heating operation of the heating system 2 with a blocked flue intake will now be explained with reference to Figure 10. Heating demand is applied by a user at to, and, as shown by model 352, after a period 41 of transient mode of operation (around 1500 seconds) the output temperature reaches a period 42 of steady mode of operation, around 88°C. The flue inlet is blocked so, as shown by model 354, the heating system 2 drives an inlet pump (such as a fan) harder to draw sufficient oxygen in for the required output power. As shown by model 353, the power required in this instance is 80%, i.e. over twice the normal level shown by 153 in FigureS (i.e. 40% for normal heating operation). Heating demand is removed by a user at te (around 6500 seconds) and the system 2 returns to quiescent state after a short period 43 of transient mode of operation. During the cycle 4, the heating system 2 works normally as seen by a user, but is at risk of ignition failing and will likely emit excess carbon monoxide, which is dangerous for the user. A fault should be diagnosed, and a need for maintenance predicted as described in greater detail below.

An exemplary method according to the disclosure will now be described with reference to Figures 4 to 6.

-12 -As illustrated in Figure 4, the method 100 comprises the device 10 receiving, at Sb, at least one time series 51, 52, 53, 54 of data 5 relating to the operation of the heating system 2 over a cycle of operation. The definition of the cycle is described in greater detail below.

At S20, the device 10 determines the stale of operation of the heating system 2 based on comparing S the received time series 51, 52, 53 or 54 with at least one corresponding model of time series of data (referred to as 151, 152, 153, 154; 251, 252, 253, 254, 255; 351, 352, 353, 354, or 355 as shown in Figures 8 to 10, as already discussed).

S20 will now be described in more detail in reference to Figure 5.

At S201, the device 10 defines the cycle of operation. The cycle of operation may comprise: a period 41 and/or43 of transient mode of operation, and a period 42 of steady mode of operation.

The cycle of operation may correspond to the cycle 4 of operation of the heating system 2. For example the cycle 4 of operation of the heating system 2 is derived from a duration taken from a time series corresponding to the heating mode of the heating system 2, such as time series 51 and/or 151.

For example the duration may be taken from a period taken from a power on' signal to a power off signal, such as a period taken from an ignition/flame on' signal or instruction to an ignition/flame off signal or instruction. The cycle 4 of operation of the heating system 2 may thus be easily defined.

In order to clearly define a cycle of operation for some of the time series, an auxiliary cycle 7 of operation corresponding to, e.g.: a cycle of operation for the components 21,22,23 or 24, and/or an operating phase of the system 2.

In some examples, the auxiliary cycle 7 may be derived from a duration taken from a first time series, such as time series 51 or 151 as explained above, and from a portion of a second time series based on the first time series. For example, the first 30% of the cycle 4 of operation of the system 2 may not be significant e.g., for the times series 53. Therefore an auxiliary cycle 7 (starting for example at 30% of the cycle 4, i.e. starting from 2000 seconds as shown in Figure 8) may be defined for the time series 53, in order for the times series 53 to be compared with times series 153, 253, or 353 more significantly.

As already stated above, in some examples, in order to determine the auxiliary cycle 7, the time series of data 5 may be parsed into a set of time periods 8 which contain data relating to different operating phases of the system 2, such as: quiescent phase, ignition phase (i.e. a sequence of actions to light the burner), space heating phase, -13 -water heating phase, and/or post combustion (i.e. clearing the combustion chamber 202 and cooling the system 2).

In some examples, the operating phases may be identified using the pattern matching approach previously described to correlate time series of data with models' of the various possible states.

Alternatively or additionally, the operating phases may be identified using specific parameters in the data 5, such as e.g. a space heating status value and/or a flame detection value. In some examples, the space heating status value may indicate start of ignition phase. In some examples, the flame detection value may indicate when ignition phase is complete.

The diagnosing may thus comprise using a timing of departure of the time series of data S from a model of time series of data 50 corresponding to a normal operation of the heating system 2 over a cycle 4 017 of operation.

At 3202, the device 10 compares, respectively: times series 51 with times model times series 151, 251 and/or 351; and/or times series 52 with times model times series 152, 252 and/or 352; and/or times series 53 with times model times series 153, 253 and/or 353; and/or times series 54 with times model times series 154, 254 and/or 354.

As shown in Figure 7, the comparing performed at 3202 comprises matching a pattern of the received time series 51, 52, 53 or 54 to a pattern of a model of the time series of data cited above.

If no matching is found at 3202, then the cycle may not be properly defined at 3201, and the process returns to 3201.

If a match is found by the device 10 at 3202, it is determined at S203 if the matching of at least one of 51, 52, 53 or 54 relates to normal operation time series 151, 152, 153, or 154.

If no matching with normal operation is found at S203, then the process is directed to S207 where the corresponding fault is diagnosed, i.e. for example a heating operation with a blocked condensate drain, or a heating operation of the heating system 2 with a blocked flue intake.

The process is directed to 3208 where it is determined if the diagnosing of a faulty operation at S207 triggers diagnosing, at S209, more precisely a fault, for example a fault of a component 21, 22, 23, or 24 of the heating system 2.

If the device 10 is configured to trigger a further diagnosing at 8209, diagnosing more precisely a fault may comprise for example, at 8209, defining a new specific cycle, parsing the data 5 and identifying a pattern of a fault, e.g., of a component 21, 22, 23, or 24, defining a new time series to compare with a new model time series, and comparing. Then the process ends and continues with S30 of Figures 4 and 6.

If it is determined at 8208 that the device 10 is not configured to trigger a further diagnosing, the process ends and continues with 830 of Figures 4 and 6.

-14 -If a matching with normal operation is found at S203, then the process is directed to 3204 where normal operation is diagnosed.

At S205, it is determined if the device 10 is configured to determine the state of the system 2 over more than one cycle 4.

S If it is determined at S205 that the device 10 is not configured to determine the state of the system 2 over more than one cycle 4, the process ends and continues with S30 of Figures 4 and 6.

If it is determined at S205 that the device 10 is configured to determine the state of the system 2 over more than one cycle 4, the process is directed to 3206 where potential trends are determined.

Identifying a trend at S206 may comprise using comparisons between cycles 4 (the cycles are not necessarily successive cycles, and any number of cycles may be considered). The process ends and continues with S30 of Figures 4 and 6.

As already stated, the method 100 also comprises determining, at 330, a course of action based on the determining of 820.

S30 will now be described in more detail in reference to Figure 6.

At S301, the device 10 determines if a faulty operation was determined at S20.

If it is determined at S301 that a faulty operation was determined at S20, the device 10 determines at 8302 if maintenance is needed and/or should be predicted, i.e. in case e.g., the fault is dangerous for the user and/or critical for the functioning of the heating system 2.8302 allows avoiding unnecessary and costly maintenance for minor faults of the system 2.

If no maintenance is needed, then the process ends.

If it is determined at S302 that a need for maintenance is predicted, then the device 10 triggers at S303 maintenance, in a timely fashion. The process then ends.

It is appreciated that centralization in the device of the data relating to the operation of a plurality of systems enables more efficient management of the plurality of systems, both economically and technically, and allows avoiding rushing costly maintenance if the maintenance can be delayed and performed in a more efficient way (e.g. for a cluster of heating systems 2 connected to the same network 3). The maintenance may thus be planned and rationalized, for both technical and economic efficiencies. The data from the plurality of systems 2 may also be used for analysis and statistics.

As already stated, the triggering may comprise outputting a maintenance instruction and/or actually maintaining the heating system, for example locally or by sending technicians to the system 2. The maintenance instruction may be sent, for example, to at least one controller such as the controller 23, locally on the system 2 and/or over the network 3. The maintenance instruction may also be sent to a user of the system, for example, via a SMS, by giving instructions to the user.

If it is determined at S301 that a faulty operation was not determined at S20, it is determined at S304 if a faulty trend was determined at S20, then the process is directed to S302 already described.

-15 -Aspects and preferred examples of the present invention are set out in the appended claims.

In another aspect, there is provided a computer program product comprising program instructions to program a processor to carry out data processing of methods according to aspects of the disclosure or to program a processor to provide controllers, devices and apparatus (comprising the device, the network and the plurality of appliances) according to aspects of the disclosure.

As one possibility, there is provided a computer program, computer program product, or computer readable medium, comprising computer program instructions to cause a programmable computer to carry out any one or more of the methods described herein. In some examples, components of the device 10 and/or the communications network 3 may use specialized applications and hardware. It is appreciated that software components of the present disclosure may, if desired, be implemented in ROM (read only memory) form. The software components may, generally, be implemented in hardware, if desired, using conventional techniques.

In example implementations, at least some portions of the activities related to the device 10 and/or the communications network 3 herein may be implemented in software.

Various features described above may have advantages with or without other features described above.

The above embodiments are to be understood as illustrative examples of the invention. Further embodiments of the invention are envisaged. It is to be understood that any feature described in relation to any one embodiment may be used alone, or in combination with other features described, and may also be used in combination with one or more features of any other of the embodiments, or any combination of any other of the embodiments. Furthermore, equivalents and modifications not described above may also be employed without departing from the scope of the invention, which is defined in the accompanying claims.

As will be apparent to the skilled in the art, the server should not be understood as a single entity, but rather refers to a physical device comprising at least a processor and a memory, the memory being comprised in one or more servers which can be located in a single location or can be remote from each other to form a distributed network (such as server farms", e.g., using wired or wireless technology).

In some examples, one or more memory elements (e.g., the data base 6 and/or the memory 12) can store data used for the operations described herein. This includes the memory element being able to -16 -store software, logic, code, or processor instructions that are executed to carry out the activities

described in the disclosure.

A processor can execute any type of instructions associated with the data to achieve the operations S detailed herein in the disclosure. In one example, the processor could transform an element or an article (e.g., data) from one state or thing to another state or thing. In another example, the activities outlined herein may be implemented with fixed logic or programmable logic (e.g., software/computer instructions executed by a processor) and the elements identified herein could be some type of a programmable processor, programmable digital logic (e.g., a field programmable gate array (FPGA), an erasable programmable read only memory (EPROM), an electrically erasable programmable read only memory (EEPROM)), an ASIC that includes digital logic, software, code, electronic instructions, flash memory, optical disks, CD-ROMs, DVD ROMs, magnetic or optical cards, other types of machine-readable mediums suitable for storing electronic instructions, or any suitable combination thereof.

The data received by the device 10 is typically received over a range of possible communications networks 3 at least such as: a satellite based communications network; a cable based communications network; a telephony based communications network; a mobile-telephony based communications network; an Internet Protocol (IP) communications network; and/or a computer based communications network.

In some examples, the communications network 3 and/or the device 10 may comprise one or more networks. Networks may be provisioned in any form including, but not limited to, local area networks (LANs), wireless local area networks (WLANs), virtual local area networks (VLAN5), metropolitan area networks (MANs), wide area networks (WANs), virtual private networks (VPNs), Intranet, Extrariet, any other appropriate architecture or system, or any combination thereof that facilitates communications in a network.

Claims

-17 -Claims 1. A method for determining a state of operation of a domestic appliance in a plurality of domestic appliances, comprising: receiving, from the domestic appliance, a time series of data relating to the operation of the domestic appliance over a cycle of operation; and determining the state of operation of the domestic appliance based on comparing the received time series with a model of time series of data corresponding to the operation of the plurality of domestic appliances over a cycle of operation.
2. The method according to claim 1, wherein the comparing comprises matching a pattern of the received time series of data to a pattern of a model of time series of data.
3. The method according to any one of claims 1 or 2, wherein the comparing comprises retrieving the model of time series of data from a database.
4. The method according to claim 3, wherein the retrieving is performed over a network.
5. The method according to any one of claims ito 4, wherein the plurality of domestic appliances is connected to a network, and wherein the receiving of the time series comprises receiving the data over the network.
6. The method according to any one of claims I to 5, wherein the plurality of domestic appliances comprises different types of domestic appliances, and wherein the model of time series of data corresponds to a type of domestic appliances.
7. The method according to claim 6, wherein determining the state of operation further comprises identifying the type of the domestic appliance in the plurality of domestic appliances, based on the received time series of data and/or on identification received from the domestic appliance.
8. The method according to any one of claims ito 7, further comprising assembling the model of time series of data based on time series of data relating to the operation of the plurality of domestic appliances, received from the plurality of domestic appliances.
9. The method according to any one of claims i to 8, further comprising storing a predetermined model of time series of data.-18 -
10. The method according to any one of claims 1 to 9, wherein the determining of the state of operation of the domestic appliance comprises diagnosing normal operation of the domestic appliance based on the comparing.

S
11. The method according to any one of claims 1 to 10, wherein the determining of the state of operation of the domestic appliance comprises diagnosing a fault of the domestic appliance based on the comparing.
12. The method according to claim 11, further comprising triggering diagnosing a fault of a component of the domestic appliance based on the diagnosing of the fault of the domestic appliance.
13. The method according to any one of claims 11 or 12, wherein diagnosing a fault comprises diagnosing heating operation of a domestic fluid heating system with a blocked condensate drain and/or heating operation of the domestic fluid heating system with a blocked flue intake.
14. The method according to any one of claims 11 to 13, wherein the diagnosing comprises: using a timing of departure of the time series of data from a model of time series of data corresponding to a normal operation of the heating system over a cycle of operation and/or displaying processed time series of data in an interface on a display screen of a device.
15. The method according to any one of claims 11 to 14, further comprising predicting a need for maintenance based on the diagnosing.
16. The method according to any one of claims 1 to 15, further comprising detecting a trend of operation of the domestic appliance, based on the determining of the state of operation of the domestic appliance over at least two cycles of operation.
17. The method according to claim 16, further comprising predicting a need for maintenance based on the detecting of the trend.
18. The method according to any one of claims 15or17, further comprising triggering maintenance of the domestic appliance based on the predicting.
19. The method according to claim 18, wherein triggering maintenance comprises outputting a maintenance instruction.

-19 -
20. The method according to any one of claims 18 or 19, wherein triggering maintenance comprises maintaining the domestic appliance.
21. The method according to any one of claims 1 to 20, wherein the cycle of operation comprises a period of transient mode of operation and a period of steady mode of operation.
22. The method according to any one of claims I to 21, comprising defining a cycle of operation based on: deriving from a duration taken from a first time series and from a portion of a second time series selected based on the first time series, and/or using specific parameters in the data.
23. The method according to claim 22, wherein the duration taken from the first time series is: a period taken from a power on signal to a power off signal; and/ the specific parameters in the data comprise: a space heating status value; and/or a flame detection value.
24. The method according to any one of claims 1 to 23, further comprising assembling the model of time series of data based on data received from the plurality of systems during a set up.
25. The method according to any one of claims 1 to 24, further comprising predefining the model of time series of data based on programming.
26. The method according to any one of claims ito 25, performed at least partly locally in the heating system.
27. The method according to any one of claims ito 26, wherein the domestic appliance is a domestic boiler and/or a washing machine.
28. The method according to any one of claims 1 to 27, wherein the received time series of data relates to the operation of a component of the domestic appliance over a cycle of operation.
29. A device configured to determine a state of operation of a domestic appliance in a plurality of domestic appliances, configured to: receive, from the domestic appliance, a time series of data relating to the operation of the domestic appliance over a cycle of operation; and -20 -determine the state of operation of the domestic appliance based on comparing the received time series with a model of time series of data corresponding to the operation of the plurality of domestic appliances over a cycle of operation.
30. The device according to claim 29, further configured to match a pattern of the received time series of data to a pattern of a model of time series of data.
31. The device according to any one of claims 29 or 30, wherein further configured to retrieve the model of time series of data from a database.
32. The device according to claim 31, further configured to retrieve the model over a network.
33. The device according to any one of claims 29 to 32, further configured to receive the time series of data over a network.
34. The device according to any one of claims 29 to 33, further configured to assign the model of time series of data to a type of domestic appliances.
35. The device according to claim 34, further configured to identify the type of the domestic appliance in the plurality of domestic appliances, based on the received time series of data and/or on identification received from the domestic appliance.
36. The device according to any one of claims 29 to 35, further configured to assemble the model of time series of data based on time series of data relating to the operation of the plurality of domestic appliances, received from the plurality of domestic appliances.
37. The device according to any one of claims 29 to 36, further configured to store a predetermined model of time series of data.
38. The device according to any one of claims 29 to 37, further configured to diagnose normal operation of the domestic appliance based on the comparing.
39. The device according to any one of claims 29 to 38, further configured to diagnose a fault of the domestic appliance based on the comparing.
40. The device according to claim 39, further configured to trigger diagnosing a fault of a component of the domestic appliance based on the diagnosing of the fault of the domestic appliance.

-21 -
41. The device according to any one of claims 39 or 40, configured to diagnose heating operation of a domestic fluid heating system with a blocked condensate drain and/or heating operation of the domestic fluid heating system with a blocked flue intake.
42. The device according to any one of claims 39 to 41, configured to predict a need for maintenance based on the diagnosing.
43. The device according to any one of claims 39 to 42, further configured to detect a trend of operation of the domestic appliance, based on the determining of the state of operation of the domestic appliance over at least two cycles of operation.
44. The device according to claim 43, further configured to predict a need for maintenance based on the detecting of the trend.
45. The device according to any one of claims 42 or 44, further configured to trigger maintenance of the domestic appliance based on the predicting.
46. The device according to claim 45, configured to trigger maintenance by outputting a maintenance instruction.
47. The device according to any one of claims 45 or 46, configured to trigger maintenance by maintaining the domestic appliance.
48. The device according to any one of claims 29 to 47, further configured to assemble the model of time series of data based on data received from the plurality of systems during a set up.
49. The device according to any one of claims 29 to 48, further configured to predefine the model of time series of data based on programming.
50. The device according to any one of claims 29 to 49, configured to be connected to the plurality of domestic appliances via a communications network.
51. The device according to any one of claims 29 to 50, configured to be at least partly located in the heating system.
52. Apparatus comprising: -22 -a monitoring device comprising a server; a plurality of domestic appliances connected to the server of the device via a communications network and adapted to send one or more time series of data relating to the operation of the plurality of domestic appliances over a cycle of operation, wherein the monitoring device is configured to: receive, from a domestic appliance in the plurality of domestic appliances, a time series of data relating to the operation of the domestic appliance over a cycle of operation; and determine the state of operation of the domestic appliance based on comparing the received time series with a model of time series of data corresponding to the operation of the plurality of domestic appliances over a cycle of operation.
53. Apparatus according to claim 52, wherein the domestic appliance is a domestic boiler and/or a washing machine.
54. Apparatus according to any one of claims 52 or 53, wherein the domestic appliance comprises a controller configured to: receive data from sensors and/or from components of the domestic appliance; and send data to the monitoring device over the network.
55. A device substantially as hereinbefore described with reference to and/or as illustrated in Figures 1,2 and/or 3 of the accompanying drawings.
56. Apparatus substantially as hereinbefore described with reference to and/or as illustrated in Figures 1,2 and/or 3 of the accompanying drawings.
57. A method substantially as hereinbefore described with reference to and/or as illustrated in Figures 4,5 and/or 6 of the accompanying drawings.
58. A computer program product comprising program instructions to program a processor to carry out data processing of a method according to any one of claims 1 to 28 or57, orto program a processor to provide a device of any one of claims 29 to 51 or 55, or to provide an apparatus of any one of claims 52 to 54 or 56.