WO2021081604A1

WO2021081604A1 - Vehicle weighing system

Info

Publication number: WO2021081604A1
Application number: PCT/AU2020/051191
Authority: WO
Inventors: Roger Butler; Ali ANAISSI
Original assignee: Commonwealth Scientific And Industrial Research Organisation
Priority date: 2019-10-31
Filing date: 2020-11-02
Publication date: 2021-05-06

Abstract

A system for measuring the weight of groups of axles on a vehicle, wherein the system includes one or more processing devices configured to receive weight signals from a weighing device configured to measure a weight of a vehicle traversing the weighing device, analyse the weight signals to detect weight change events, use the one or more weight change events to identify entry events indicative of axles entering the weighing device, use the one or more weight change events to identify exit events indicative of the axles exiting the weighing device, identify groups of axles at least partially based on a timing of the entry events or the exit events and use the weight signals and the identified groups of axles to determine a weight for at least one group of axles.

Description

VEHICLE WEIGHING SYSTEM

Background of the Invention

[0001] The present invention relates to a method and system for determining the weight of at least one group of axles of a vehicle as the vehicle traverses a weighing device.

Description of the Prior Art

[0002] The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that the prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

[0003] Traditional, simple, weighing devices arc capable of measuring the weight of an object but are unable to accurately measure items being transported, require manual entry from the user which may be slow and/or unreliable and do not satisfy compliance requirements under Chain of Responsibility obligations under Australian law. Attempts have been made to automate weighing of vehicles.

[0004] US6459050 discloses an apparatus and method for converting in-ground static weighing scales for vehicles to weigh-in-motion systems. The apparatus upon conversion includes the existing in-ground static scale, peripheral switches and an electronic module for automatic computation of the weight. By monitoring the velocity, tyre position, axle spacing, and real time output from existing static scales as a vehicle drives over the scales, the system determines when an axle of a vehicle is on the scale at a given time, monitors the combined weight output from any given axle combination on the scale(s) at any given time, and from these measurements automatically computes the weight of each individual axle and gross vehicle weight by an integration, integration approximation, and/or signal averaging technique. [0005] US3842922 discloses a truck weighing scale capable of measuring various axle configurations and providing individual axle weights and total truck weights. The individual axle weights are determined by successively accumulating total weights each time an axle enters the scale and subtracting the next preceding accumulated axle weight.

[0006] WO2001027569 discloses a method and a system of weighing a vehicle in motion which includes a load sensing device (particularly in the forms of an optic fibre cable or strain gauges) located beneath the surface of a roadway and extending across at least one lane and processing means (86) for receiving a signal from the load sensing device and for providing an indication of the vehicle weight. The load sensing device may be located on an extruded substrate member (2) that is located inside a hollow conduit. A plurality of runs of optic fibre may run across a single lane; additionally, an axle detector in the form of a piezoelectric strip or an optic fibre cable may be included in parallel. An optic fibre system may include a light source (60), a sensing fibre (10), a reference fibre (12), a coupler (70) connecting the fibres and processing means (86) for analysing the light and determining the weight of the vehicle. In accordance with one particular disclosed method of installation, an entry trench is dug beside a roadway to enable the boring of a borehole beneath the roadway, then a hollow conduit having a load sensing device supported on a substrate member (2) inside the conduit is introduced in the borehole and finally the borehole is filled with filler material.

[0007] However, these approaches suffer from drawbacks and in particular require sensors in addition to the weighing device in order to detect vehicle movement and/or axle locations. Adding additional sensors requires pre-existing measuring devices to be retrofitted, which may be costly or impractical. Further, the additional sensors or similar complexities will require additional maintenance and introduce additional points of physical failure within the system.

Summary of the Present Invention

[0008] In one broad form, an aspect of the present invention seeks to provide a system for measuring the weight of groups of axles on a vehicle, wherein the system includes one or more processing devices configured to: receive weight signals from a weighing device configured to measure a weight of a vehicle traversing the weighing device; analyse the weight signals to detect weight change events; use the one or more weight change events to identify entry events indicative of axles entering the weighing device; use the one or more weight change events to identify exit events indicative of the axles exiting the weighing device; identify groups of axles at least partially based on a timing of the entry events or the exit events; and, use the weight signals and the identified groups of axles to determine a weight for at least one group of axles.

[0009] In one embodiment, the one or more processing devices are configured to detect weight change events by using the weight signals to determine changes in total weight measured by the weighing device.

[0010] In one embodiment, the one or more processing devices are configured to detect gaps between the one or more groups of axles using weight signals.

[0011] In one embodiment, the one or more processing devices are configured to: detect gap durations between weight change events; and, use the gap durations to identify groups of axles.

[0012] In one embodiment, the one or more processing devices are configured to: determine a gap duration between successive weight change events; and, one of: if the gap duration is less than a threshold, determine the gap is a gap between axles in a group of axles; and, if a gap duration is more than a threshold, determine the gap is a gap between groups of axles.

[0013] In one embodiment, the one or more processing devices are configured to: determine a mean gap duration between all weight change events; and, determine the threshold using the mean gap.

[0014] In one embodiment, the mean gap duration is a moving mean gap duration.

[0015] In one embodiment, the threshold is at least one of: a static threshold; and, a dynamic threshold. [0016] In one embodiment, the one or more processing devices are configured to determine gap weights using the weight signals, wherein the gap weights are indicative of a total weight measured by the weighing device in gaps between the weight change events.

[0017] In one embodiment, the one or more processing devices are configured to subtract consecutive gap weights to determine the weight for axles entering or exiting the weighing device between the consecutive gap weights.

[0018] In one embodiment, the one or more processing devices are configured to identify: group entry events, wherein each group entry event is a group of entry events indicative of a group of axles entering the weighing device; and, group exit events, wherein each group exit event is a group of exit events indicative of groups of axles exiting the weighing device.

[0019] In one embodiment, the one or more processing devices are configured to determine the weight of a group of axles using at least one of the group entry events and the group exit events for the group of axles.

[0020] In one embodiment, the one or more processing devices are configured to subtract gap weights before and after group entry or group exit events for a group of axles to determine the weight for the group of axles entering or exiting the weighing device.

[0021] In one embodiment, the one or more processing devices are configured to analyse the group entry events and the group exit events independently.

[0022] In one embodiment, the one or more processing devices are configured to: determine one or more matched events, wherein the one or more matched events are entry events for an axle that correspond to exit events for the same axle; and, determine the weight of at least one group of axles at least partially by analysing the one or more matched group events simultaneously.

[0023] In one embodiment, the one or more processing devices are configured to: determine one or more matched group events, wherein the one or more matched group events are group entry events for a group of axles that correspond to group exit events for the same group of axles; and, determine the weight of the group of axles at least partially by analysing the one or more matched group events simultaneously.

[0024] In one embodiment, the one or more processing devices are configured to determine the weight of multiple groups of axles at least partially by solving a set of linear equations based on multiple matched group events.

[0025] In one embodiment, the linear equations are overdetermined linear equations.

[0026] In one embodiment, the linear equations are solved in a forward and a reverse direction independently.

[0027] In one embodiment, the one or more processing devices are configured to determine the weight of the at least one group of axles at least partially by solving a least squares formulation based on the one or more matched group events.

[0028] In one embodiment, the one or more processing devices are configured to detect if there is a mismatch between at least one of: a number of entry events and number of exit events; a number of group entry events and number of group exit events; a weight of matched entry and exit events; a weight of matched group entry events and group exit events; a total weight of entry events and a total weight of exit events; and, a total weight of group entry events and total weight of group exit events.

[0029] In one embodiment, the one or more processing devices are configured to display an error to a user through a user interface if a mismatch or abnormal weight is detected.

[0030] In one embodiment, the one or more processing devices are configured to provide an output to a user indicative of at least one of: a weight of at least one group of axles; and, a vehicle weight.

[0031] In one embodiment, the weighing device is at least as long as a group of axles. [0032] In one embodiment, the vehicle traverses the weighing device substantially continuously.

[0033] In one broad form, an aspect of the present invention seeks to provide a method for measuring the weight of one or more groups of axles, wherein the method includes one or more processing devices: receiving weight signals from a weighing device configured to measure a weight of a vehicle traversing the weighing device; analysing the weight signals to detect one or more weight change events; using the one or more weight change events to identify one or more entry events, wherein the one or more entry events are where one or more axles enter the weighing device; using the one or more weight change events to identify one or more exit events, wherein the one or more exit events are where the one or more axles exit the weighing device; identifying groups of axles at least partially based on a timing of the one or more entry events or the one or more exit events; and, using the weight signals and the identified groups of axles to determine a weight for the one or more groups of axles.

[0034] It will be appreciated that the broad forms of the invention and their respective features can be used in conjunction and/or independently, and reference to separate broad forms is not intended to be limiting. Furthermore, it will be appreciated that features of the method can be performed using the system or apparatus and that features of the system or apparatus can be implemented using the method.

[0035] It will be appreciated that the broad forms of the invention and their respective features can be used in conjunction and/or independently, and reference to separate broad forms is not intended to be limiting. Furthermore, it will be appreciated that features of the method can be performed using the system or apparatus and that features of the system or apparatus can be implemented using the method.

Brief Description of the Drawings

[0036] Various examples and embodiments of the present invention will now be described with reference to the accompanying drawings, in which: [0037] Figure 1 is an example of a schematic drawing of the system for measuring the weight of at least one group of axles;

[0038] Figure 2 is an example of a flow chart for a method for measuring the weight of at least one group of axles;

[0039] Figure 3 is an example of a schematic drawing for a processing device;

[0040] Figure 4 is an example of a graph demonstrating weight changes measured by the measuring device as a vehicle traverses the measuring device;

[0041] Figure 5A is an example of a flow chart for analysing the weight signals generated by the measuring device; and,

[0042] Figure 5 B is an example of a flow chart for analysing the weight signals generated by the measuring device.

Detailed Description of the Preferred Embodiments

[0043] An example of a system for use in determining the weight of at least one group of axles for a vehicle will now be described with reference to Figure 1.

[0044] For the purpose of explanation, reference to a weight of axles or a weight of a group of axles (or any other similar phrasing) includes the weight of the axles themselves as well as weight the axles support.

[0045] In this example, the system 100 includes one or more processing devices 110 coupled to a weighing device 120, configured to measure a weight of the vehicle 130 traversing the weighing device 120.

[0046] The nature of the weighing device 120 will vary depending on the preferred implementation and the nature of the vehicle. In this example, the weighing device 120 is configured to be used to weigh vehicles such as trucks, lorries, road trains or similar, and is in the form of a weighbridge including a panel which may be integrated into a bridge, roadway, or other similar structure, so that the vehicle 130 may traverse the weighing device 120 allowing the weight measurement to be performed. In the current example, the weighing device 120 may be shorter than the length of the vehicle's trailer but other suitable sizes at least as long as the group of axles to be measured can be used. It will be appreciated from the following however, that this is not essential and the techniques can be applied to other vehicles and corresponding weighing devices, such as a rail based weighing device for measuring weights of rail vehicle, such as trains and/or rolling stock.

[0047] The one or more processing devices 110 may be external to the weighing device 120 and vehicle 130 (as shown in Figure 1) and in communication with the weighing device 120 and optionally the vehicle 130, for example using wired and/or wireless communication, or the like. Alternatively, the one or more processing devices 110 can be integrated into the weighing device 120, or vehicle 130, and other suitable arrangements could also be used. The one or more processing devices 110 could be of any suitable form, and could include a custom or off-the- shelf processing device and/or a suitably programmed general purpose processing system, such as a computer system, or the like. For ease of illustration the remaining description will refer to a processing device, but it will be appreciated that multiple processing devices could be used, with processing distribution between processing devices as needed, and that reference to the singular encompasses the plural arrangement and vice versa.

[0048] In use, the processing device 110 uses signals from the weighing device 120 to determine the weight of groups of axles of the vehicle 130. In this example, the vehicle includes one or more axles 131 and one or more groups of axles 132, the groups of axles including at least one axle. In this example, the vehicle includes a truck, two trailers, twelve axles and five groups of axles. It will be appreciated that any size or layout of vehicle may be measured by the system 100 and the above example is for the purpose of illustration only.

[0049] An example of a method for using the system 100 of Figure 1 to measure the weight of groups of axles on a vehicle will now be described with reference to Figure 2. [0050] In this example, at step 200, the vehicle 130 drives over the weighing device 120. At step 210 the weighing device 120 measures the weight of the vehicle as it traverses the weighing device 120. This allows the system 100 to measure changes in weight as the vehicle traverses the weighing device 120, without requiring the vehicle 130 to stop to take measurements. The weighing device 120 then generates weight signals, indicative of the changes in weight as the vehicle 130 traverses the weighing device 120, which are received by the processing device 110 at step 220. An example weight signal for a vehicle traversing a weigh device is shown in Figure 4, and will be described in more detail below.

[0051] At step 230, the processing device 110 analyses the received weight signals. The analysis typically involves analysing changes in the measured weight over time to determine weight change events, which can then be used to identify entry and exit events that are indicative of when an axle enters or exits the weighing device 120 at step 240. Specifically, the processing devices 110 may determine that an axle enters and/or exits the weighing device 120 when there is a change of weight over a short period of time, with an increase in weight being indicative of an entry event and a decrease in weight indicative of an exit event.

[0052] Using the identified entry and exit events, the processing device 110 identifies groups of axles at step 250. For example, the processing devices 110 may identify a group of axles where one or more axle entry and/or exit events occur in a short period of time thereby indicating that the axles are part of the same group.

[0053] At step 260, the processing device 110 determines the weight of groups of axles by using the identified groups of axles and the measured changes in weight. At a basic level, this could include simply subtracting pre and post entry/exit event weights, to determine the change in weight when an axle group enters or exits the weighing device. However, additionally and/or alternatively, the processing device 110 may determine the weight using other techniques, including adding the relative weights of the axles identified as part of the group of axles. The determination may include other techniques and/or methods depending on the implementation of the system 100, as will be described in more detail below. [0054] Accordingly, the above described system 100 can allow weight measurements to be performed on vehicles traversing a weighing device, and in particular allows for the determination of the weight of a group of axles solely through examination of the weight signals generated by the weighing device 120. This removes the need for additional sensors and allows the system 100 to be easily retrofitted into pre-existing weighing devices 120, for example, through a software update to a processing device associated with the weighing device, or through the addition of a suitably programmed processing device.

[0055] Furthermore, as the system can detect the vehicle configuration solely through analysis of the weight signals this avoids the need for the vehicle 130 to traverse the weighing device at any specific speed and also avoids the need for the vehicle to have a known layout, allowing this to be used in a wide range of different situations.

[0056] A number of further features will now be described.

[0057] In one example, the processing device 110 is configured to detect weight change events by using weight signals to determine changes in total weight measured by the weighing device 120. The system 100 may be attempting to determine weight change events where the measuring device 120 can only measure a single, total weight. By determining weight change events based on changes in total weight, this allows the processing device 110 to detect weight change even when the weighing device 120 cannot weigh each axle individually.

[0058] In one example, the processing device 110 is configured to detect gaps between entry and/or an exit events. As the vehicle 130 traverses the measuring device 120, there may be measurable periods of time where the processing device 110 does not detect a weight change, and hence an entry and/or an exit event. The gaps can then be used in identifying whether or not an axle is within a group, as well as allowing weights of axles and/or groups of axles to be identified.

[0059] For example, the processing device 110 may be configured to detect gap durations between entry and/or exit events. Small gaps may indicate that consecutive entry and/or exit events are indicative of axles that may be part of a group of axles, whereas large gaps may indicate that consecutive entry and/or exit events are indicative of axles that may not be part of a group of axles. Thus, the processing device 110 can determine groups of axles at least partially based on the determined gaps, allowing the system 100 to be able to determine groups of axles without input from a user and may also determine groups of axles at higher accuracy and efficiency.

[0060] In one example, the processing device 110 uses the duration of the gaps and a threshold to determine whether the gap corresponds to a gap between groups of axles (an intergroup spacing) or between axles in a group (an intragroup spacing). Thus, if a gap duration is greater than a threshold, this could be indicative of an intergroup spacing, whereas if the gap is less than a threshold this could be indicative of an intragroup spacing. As a speed of the vehicle is not known, the threshold could be based on a mean gap duration, so a longer than mean gap implies an intergroup spacing, whilst a shorter gap duration implies an intragroup spacing,

[0061] For example, the processing device 110 may determine the mean gap is 100ms, if the processing device 110 determines that a gap is 20ms (which is significantly shorter than the mean gap) then it may indicate the 20ms gap corresponds to intragroup spacing between two axles within the same group of axles. Conversely, if the processing device 110 determines that a gap is 500ms (which is significantly longer than the mean gap) then it may indicate the 500ms gap corresponds to intergroup spacing between two different groups of axles. Therefore, detecting gaps between groups of axles using weight signals may improve the accuracy of system 100.

[0062] The processing device 110 may also use the duration of the gaps to determine the speed of the vehicle 130 which may assist the processing device 110 when analysing the weight signals. For example, compensating for the speed of the vehicle may simplify the remaining analysis, allowing for the system 100 to more accurately measure the weight of the group of axles and allow for the vehicle 130 to traverse the weighing device 120 at a higher speed. [0063] In one example, the processing device 110 is configured to determine gap weights by using weight signals, wherein the gap weights are the total weight measured by the weighing device in gaps between exit and/or entry events. Further, the processing device 110 may be configured to subtract consecutive gap weights to determine the weight for individual axles and/or groups of axles based on gap weights before and after entry or exit of a group of axles. By determining changes in gap weights and/or determining the weight for the group of axles using consecutive gap weights, the system 100 removes the need to determine a zero weight as measured by the weighing device 120. This reduces the time needed to weigh the vehicle 130 as calibration tests do not need to be conducted prior to using the system 100. Further, this removes the inaccuracy of a potentially inaccurate and/or incorrect calibration as the processing devices 110 are determining the weight of the group of axles using changes in weights rather than absolute weights.

[0064] In one example, the processing device 110 is configured to identify group entry events and group exit events, wherein group entry/exit events are indicative of groups of axles entering/exiting the weighing device. For example, if the group of axles 132 (comprising of three axles 131) enter the weighing device 120, this may be represented by three entry events in quick succession. The processing device 110 may then identify the three entry events to be part of the same group entry event. This allows the system 100 to identify and determine the weight of the group of axles at higher accuracy. As mentioned above, this can be performed by analysing gap durations and/or could be performed by clustering entry/exit events.

[0065] In one example, the processing device 110 is configured to determine the weight of the groups of axles at least partially by analysing the group entry and group exit events, and in one particular example, by analysing the group entry and group exit events independently. The processing device 110 may subtract consecutive gap weights within the group entry or group exit events to determine the weight of the group of axles corresponding to the group entry or group exit events. This may assist the system 100 where there is incomplete information, or may improve the speed of the system 100 determining the weight of the group of axles. [0066] In another example, the processing device 110 is configured to determine one or more matched events and/or one or more matched group events, wherein the one or more matched events are entry events that correspond to exit events for the same axle and wherein the one or more matched group events are group entry events that correspond to group exit events for the same group of axles. By matching events, it reduces the chance that events are incorrectly identified by the processing device 110 and increases the accuracy of analysis.

[0067] For example, as the vehicle 130 traverses the weighing device 120 it may result in an entry event followed by multiple entry and exit events occurring in swift succession. Without matching events, the system 100 may be unable to accurately determine the nature of events that occur in swift succession, such as an entry and exit event that occur simultaneously, thereby effectively nullifying each other. For example, if an entry and exit event occur simultaneously, nearly simultaneously or the like, the system 100 may be confused by conflicting results and incorrectly identify the events. However, if the processing device 110 matches events while analysing the weight signals, it will be more likely to recognise that two events have occurred. For example, the processing device 110 will be more likely to recognise an exit event, even if it is obscured by an entry event, if it is looking for an exit event to match, and hence is more likely to accurately identify the events.

[0068] Matching events may also allow the system 100 to determine if there is insufficient information provided by the weight signals. For example, if there is an event that cannot be matched, it indicates that an event was not measured, for example as a result of the vehicle 130 not completely traversing the weighing device 120, or the like.

[0069] In one example, the processing device 110 is configured to determine the weight of the at least one group of axles at least partially by analysing the one or more matched group events simultaneously. By analysing matched group events simultaneously, it reduces the number of unknown variables, thereby improving accuracy and consistency for determining the weight of the at least one group of axles. [0070] In one particular example, the processing device 110 is configured to determine the weight of the at least one group of axles at least partially by solving a set of linear equations based on the match group events. The processing device 110 may also be configured to solve overdetermined linear equations, wherein more information is available than necessary to solve the linear equations. External factors (which may include driver error and/or other uncontrollable events) may influence the weight signals provided to the processing device 110. If the processing device 1 10 is capable of solving overdetermined linear equations, it increases the consistency of the system 100 where external factors may otherwise adversely influence the calculated weights.

[0071] Additionally, and/or alternatively, the processing device 110 may be configured to solve the linear equations in a forward and reverse direction independently. Where external factors may influence the weight signals provided to the processing device 110, the processing device 110 may attempt to solve the linear equations multiple times in different orders. Should the linear equations be solved using inconsistent or inaccurate weight signals, different results may be calculated when solving the linear equations in different orders. The system 100 may then determine that there are errors in the weight signals, allowing for more accurate and consistent results.

[0072] In one example, the processing device 110 is configured to determine the weight of the at least one group of axles at least partially by solving a least squares formulation based on the one or more matched group events. Simultaneous equations may be translated into a matrix form, allowing the processing device 110 to solve for the weight of the groups of axles simultaneously. This allows the system 100 to determine weights faster, while using less processing power. Further, it also allows the system 100 to determine the weights of groups of axles for larger vehicles in a manner that is more efficient than solving simultaneous equations through traditional methods. In addition, solving simultaneous equations allows for more accurate weight estimates and may allow the system 100 to identify when it determines inaccurate estimates. For example, if the system 100 determines weights using a single equation, or multiple non- simultaneous equations, it may result in the system 100 relying on an inaccurate estimate and the system 100 would be unable to determine that the estimate is inaccurate as it has no other relevant data to compare the estimate to. However, if the system 100 determines weights by solving simultaneous equations, the system 100 will determine multiple estimates. If the estimates are within an acceptable, or pre-set, threshold (which may be static or dynamic), then the system 100 may average the estimates and arrive at consistently accurate results. If the estimates are not within an acceptable, or pre-set, threshold (which may be static or dynamic), then the system 100 may still average the estimates (determining results which would be more accurate than if the system 100 solved a single equation or multiple non-simultaneous equations) or generate an error indicating that the data measured by the measuring device 120 is insufficient to accurately determine the weight of the group of axles. The processing device 110 may use known functions in programming languages (such as Python, C, C++, R and/or the like) to perform the least squares solver.

[0073] In one example, the processing device 110 is configured to detect if there is a mismatch between at least one of: a number of entry events and number of exit events; a number of group entry events and number of group exit events; a weight of entry events and weight of exit events; a weight of group entry events and weight of group exit events; and/or the like. The vehicle 130 (regardless of size, number of axles and/or number of groups of axles) will always have the same number of entry events to exit events, entry weight to exit weight. For example, should the weight signals indicate that there is an uneven number of weight of the events, then the system 100 may not conduct a full analysis and/or the like to prevent outputting inaccurate results.

[0074] In one example, the processing device 110 is configured to display an error to a user through a user interface. The error may include if there is a mismatch of events and/or weights, weight signals that indicate measurements that are outside reasonably expected norms and/or the like. Displaying the error to the user may inform the user of an issue and allow that issue to be corrected. Additionally or alternatively, the processing device 110 is configured to output feedback to the user. The feedback may include errors, methods for rectifying errors, informing the user the vehicle 130 must be reweighed, outputting results of the weighing, such as an axle group weight, vehicle weight, or the like.

[0075] In one example, the weighing device 120 is at least as long as a group of axles 132. Including a larger weighing device allows for more of the vehicle 130 at once. This will allow for less overlap between entry and exit events, therefore generating weight signals that are more accurate and quicker to analyse.

[0076] In one example, the vehicle 130 transverses the weighing device 120 substantially continuously, and ideally at a relatively constant speed. Thus, the system 100 may allow for the vehicle to continuously move over the weighing device 120 at high speed without needing to significantly slow down or stop while conducting the measuring. This allows the system 100 to determine the weight of groups of axles while interfering with the vehicle 130 as little as possible and reducing the time needed. However, this is not essential and, in some examples, the vehicle can move at a varying speed and/or non-continuously, for example, allowing the vehicle to stop whilst traversing the weighing device 120 as needed.

[0077] An example of a processing device 110 will now be described with reference to Figure 3.

[0078] In this example, the processing device 110 includes at least one microprocessor 300, a memory 301, an optional input/output device 302, such as a keyboard and/or display, and an external interface 303, interconnected via a bus 304 as shown. In this example the external interface 303 can be utilised for connecting the processing system 110 to peripheral devices, such as the weighing device 120, databases, other storage devices, or the like. Although a single external interface 303 is shown, this is for the purpose of example only, and in practice multiple interfaces using various methods (e.g. Ethernet, serial, USB, wireless or the like) may be provided.

[0079] In use, the microprocessor 300 executes instructions in the form of applications software stored in the memory 301 to allow the required processes to be performed. The applications software may include one or more software modules, and may be executed in a suitable execution environment, such as an operating system environment, or the like.

[0080] Accordingly, it will be appreciated that the processing device 110 may be formed from any suitable processing system, such as a suitably programmed client device, PC, web server, network server, or the like. In one particular example, the processing device 110 is a standard processing system such as an Intel Architecture based processing system, which executes software applications stored on non-volatile (e.g., hard disk) storage, although this is not essential. However, it will also be understood that the processing system could be any electronic processing device such as a microprocessor, microchip processor, logic gate configuration, firmware optionally associated with implementing logic such as an FPGA (Field Programmable Gate Array), or any other electronic device, system or arrangement.

[0081] The processing device 110 acts to determine the weight of at least one group of axles based on weight signals provided by the weighing device 120. To achieve this, the system 100 typically executes analysis with actions performed by the processing device 110, being performed by the microprocessor 300 in accordance with instructions stored as applications software in the memory 301 and/or input commands received from a user via the I/O device 302.

[0082] An example of a graph for entry and exit events will now be described with reference to Figure 4.

[0083] The graph shows a diagrammatic example of the weights measured by the weighing device 120 while a vehicle 130 traverses the weighing device 120. In this example, the vehicle 130 contains four groups of axles, the first group containing the steering axle and groups two, three and four containing two axles each.

[0084] Initial weight 401 shows the measuring device 120 prior to the vehicle 130 traversing the weighing device 120. The first weight increase 402 is caused by the steering axle of group one entering the weighing device 120. The second and third weight increases 403, 404 are caused by the axles of group two entering, whilst fourth and fifth weight increases 405, 406 are caused by the axles of group three entering. Finally, sixth and seventh weight increases 409, 411 are caused by the axles of group four entering. The first weight decrease 407 is caused by the steering axle of group one exiting the weighing device 120. The second and third weight decreases 408, 410 are caused by the axles of group two exiting, the fourth and fifth weight decreases 412, 413 are caused by the axles of group three exiting, and sixth and seventh weight decreases 414, 415 are caused by the axles of group four exiting. Final weight 416 shows the measuring device 120 returning to measuring near zero weight as the vehicle 1 0 has finished traversing.

[0085] An example of a flow chart for analysing weight signals will now be described with reference to Figures 5A and 5B.

[0086] At step 500 the processing device 110 analyses the weight signals it has received from the weighing device. At step 505, the processing device 110 identifies where the weight changes over time, and from the identified weight changes, the processing device identifies entry and exit events at step 510, specifically identifying the individual weight increases 402, 403, 404, 405, 406, 409, 411 and decreases 407, 408, 410, 412, 413, 414, 415. The entry and exit events are indicative of where axles enter or exit the weighing device 120 as the vehicle 130 traversed the weighing device 120.

[0087] At step 515, the processing device 110 detects if there are any mismatches between the number and/or weight of entry and exit events as well as any other potential errors or inconsistencies. At step 520, the system 100 displays any errors to the user, which may be through a user interface.

[0088] At step 525, the processing device 110 analyses the entry and exit events and determines which events may be part of the same group of axles. The processing device 110 may determine the frequency of successive entry and/or exit events, where the frequency exceeds a static threshold, it may indicate the events may be part of the same group of axles. For example, if the vehicle 130 is travelling at 2m/s the static threshold may be 50ms. The system 100 may then determine that multiple entry events are part of the same group of axles if two or more entry events occur within 50ms. Alternatively, a dynamic threshold may be used, where the dynamic threshold is based on a moving average that changes depending on the frequency of events resulting in a further increase in accuracy. When the vehicle 130 moves at an inconsistent speed while traversing the measuring device 120, a static threshold may be insufficient to accurately classify successive events. For example, if the vehicle begins travelling at 2m/s, the static threshold may be 50ms. If the vehicle then accelerates to 4m/s, the static threshold would not change and remain at 50ms. This may result in two or more entry events being classified as part of the same group of axles even if they are not. However, if the system 100 uses a dynamic threshold that accounts for changes in the frequency of events, the system 100 may be able to account for changes in the speed of the vehicle 130 and prevent misclassifications of events.

[0089] At step 530, the processing device 110 detects gaps between the identified groups of axles, which may be where there arc extended periods of time where there arc no significant changes in weight. At step 535, the processing device 110 determines gap weights, which are the weights measured by the weighing device 120 where there are no significant changes in weight.

[0090] At step 540, the processing device 110 identifies entry and exit events which may correspond to the same group of axles and groups entry and exit events. The processing device 110 may match events and/or groups of events sequentially. For example, when an entry event occurs, the processing device 110 may match the entry event to the next exit event that occurs. In a more complex example, if three entry events occur within 100ms (which may correspond to a group entry event) the processing device 110 may match the three entry events to the next three exit events that similarly occur within 100ms (which may correspond to a group exit event).

[0091] At step 545, the processing device 110 matches group entry events to their corresponding group exit events, such that the entry events for a group of axles are matched to the exit events for the same group of axles. At steps 550, 555 and 560 the processing device 110 analyses the matched group entry and group exit events either independently, through solving simultaneous equations or solving a least squares formulation. [0092] At step 555, the processing device 110 analyses the matched group entry and group exit events through solving simultaneous equations. In this example, the vehicle 130 has nine groups of axles, where the first group of axles leaves the weighing device 120 after three groups of axles have entered the weighing device 120. The sample equations are shown below: xO = 0 (1) xl = xO + ini (2) x2 = xl + in2 (3) x3 = x2 + m3 (4) x4 = x3 — outl (5) x5 = x4 + in4 - out2 (6) x6 = x5 + inS — out3 (7) x7 = x6 + in6 - out 4 (8) x8 = x7 + in7 — outS (9) x9 = x8 + in8 — out6 (10) xlO = x9 + in9 — out7 (11) xll = xlO — out8 (12) xl2 = xll — out9 = 0 (13)

[0093] Equation 1 is the initial weight measured by the weighing device 120 and in this example is assumed to be zero. Equations 2, 3, 4 are the weights measured as groups of axles enter the weighing device 120. Equations 5, 12 are the weights measured as groups of axles exit the weighing device 120. Equations 6, 7, 8, 9, 10, 11 are the weights measured as groups of axles enter or exit the weighing device 120 simultaneously or nearly simultaneously. Equation 13 is the final weight measured by the weighing device 120 once all groups of axles have exited and in this example is assumed to be zero. The equations can be solved in the forward direction, as described, or in the reverse direction, starting with equation 13. Solving in both directions allows discrepancies to be identified. [0094] At step 560, the processing device 110 analyses the matched group entry and group exit events through a least squares solver. For the purpose of clarity, the example used to describe step 555 will also be used here. Once the simultaneous equations have been prepared by the processing device 110, the processing device 110 may translate the simultaneous equations into a matrix form of Ax=B, as shown below:

A x B

[1 0 0 000000] [in/outl] [xl - xO]

[0 1 0 000000] [in/out2] [x2 - xl]

[0 0 1 000000] [in/out3] [x3 - x2] [0 -1 0 1 0 0 0 0 0] [in/out4] [x5 - x4]

[0 0 -1 0 1 0 0 0 0] [in/out5] [x6 - x5]

[0 0 0 -1 0 1 0 0 0] [in/out6] [x7 - x6]

[0 0 0 0 -1 0 1 0 0] [in/out7] = [x8 - x7]

[0 0 0 00 -1 0 1 0] [in/out8] [x9 - x8]

[0 0 0 000 -1 0 1] [in/out9] [xl0 - x9] [0 0 0 0000 -1 0] [xll - xlO]

[0 0 0 00000 -1] [xl2 - xl 1]

[0095] Column Ά' is an identity matrix. Column 'c' are unknown gap weights. Column 'B' are known weights measured by the measuring device 120. The processing device 110 may then use a least squares solver (which may be conducted using known functions in Python, C, C++, R, and/or the like) to determine the unknown gap weights in column 'c', using the determined gap weights to determine the weight of the groups of axles.

[0096] At step 565, the processing device 110 determines the weight for a group of axles. At step 570 outputs feedback to the user, which may also be displayed through a user interface.

[0097] Accordingly, the above described system allows axle group weights to be determined by measuring weight changes as a vehicle traverses a weighing device. The system can be flexible, and in one example, may be fitted into a new weighing device or may be retrofitted into pre existing weighing devices. Due to the adaptive analysis model, the weighing device does not need to be a specific size or length and so may accommodate vehicles that are longer, the same size or shorter than the weighing device. [0098] The system may determine gaps between successive events, and use those gaps to identify gaps between groups of axles rather than identify gaps between axles. The processing device may determine the nature of these gaps by determining the mean gaps between events, and then identifying whether a gap is indicative of intergroup or intragroup spacing. As intragroup spacing is typically much shorter than intergroup spacing, the processing device can reliably identify the nature of the gaps between events.

[0099] Once the system identifies gaps between groups of axles, the system may determine the weights of these gaps. Gap weights may then be subtracted to determine the weight of a group of axles. It is advantageous to determine the relative gap weights of the system over attempting to determine a raw weight of each group of axles as it removes the need to guarantee accuracy of the weighing device prior to attempting to measure a vehicle.

[0100] Determining gap weights is also more accurate than attempting to estimate individual axle weights. Depending on the truck layout and the size of the weighing device, entry and exit events may occur in swift succession or simultaneously, and so attempting to estimate the weight associated with each individual axle is likely to be inaccurate due to multiple events interfering with each other. This interference has a much lower impact when measuring gap weights, as these are weights measured in periods where no events are occurring.

[0101] The system may also further analyse the events, attempting to match and filter entry events to exit events. For example, the system may match the number of entry events to the number of exit events and/or match the weight measured in entry events to the weight measured in exit events. If a mismatch is detected, this can be communicated to the user (which may be through a user interface). Depending on the severity of the mismatch, the system may also provide suggestions to the user or feedback. For example, this may be to inform the user that the measurement must be taken again due to inaccuracy or the results provided by the system may be inaccurate and possibly detail the level of inaccuracy to the user, allowing them to determine if another measurement should be taken. [0102] Depending on the implementation sought, the weight signals provided to the processing device may be analysed in different ways. If quicker results are desired, the processing devices may analyse entry and exit events independently, seeking the first reasonable solution. If speed is less desired, the processing devices may derive a set of linear equations or overdetermined linear equations and attempt to solve them for more accurate results. If accuracy and consistency is the most important consideration, the processing devices may derive simultaneous equations, transform the simultaneous equations into a matrix and attempt to solve the matrix to determine weights of groups of axles using a least squares solver. The least squares solver process also allows for the most efficient and scalable method for performing the analysis, particularly where the system is measuring a larger vehicle.

[0103] Throughout this specification and claims which follow, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated integer or group of integers or steps but not the exclusion of any other integer or group of integers. As used herein and unless otherwise stated, the term "approximately" means ±20%.

[0104] Persons skilled in the art will appreciate that numerous variations and modifications will become apparent. All such variations and modifications which become apparent to persons skilled in the art, should be considered to fall within the spirit and scope that the invention broadly appearing before described.

Claims

THE CLAIMS DEFINING THE INVENTION ARE AS FOLLOWS:

1) A system for measuring the weight of groups of axles on a vehicle, wherein the system includes one or more processing devices configured to: a) receive weight signals from a weighing device configured to measure a weight of a vehicle traversing the weighing device; b) analyse the weight signals to detect weight change events; c) use the one or more weight change events to identify entry events indicative of axles entering the weighing device; d) use the one or more weight change events to identify exit events indicative of the axles exiting the weighing device; e) identify groups of axles at least partially based on a timing of the entry events or the exit events; and, f) use the weight signals and the identified groups of axles to determine a weight for at least one group of axles.

2) A system according to claim 1, wherein the one or more processing devices are configured to detect weight change events by using the weight signals to detennine changes in total weight measured by the weighing device.

3) A system according to claim 1 or claim 2, wherein the one or more processing devices are configured to detect gaps between the one or more groups of axles using weight signals.

4) A system according to claim 3, wherein the one or more processing devices are configured to: a) detect gap durations between weight change events; and, b) use the gap durations to identify groups of axles.

5) A system according to claim 4, wherein the one or more processing devices are configured to: a) determine a gap duration between successive weight change events; and, b) one of: i) if the gap duration is less than a threshold, determine the gap is a gap between axles in a group of axles; and, ii) if a gap duration is more than a threshold, determine the gap is a gap between groups of axles.

6) A system according to claim 5, wherein the one or more processing devices are configured to: a) determine a mean gap duration between all weight change events; and, b) determine the threshold using the mean gap.

7) A system according to claim 6, wherein the mean gap duration is a moving mean gap duration.

8) A system according to claim 6 or claim 7, wherein the threshold is at least one of: a) a static threshold; and, b) a dynamic threshold.

9) A system according to any one of the claims 1 to 8, wherein the one or more processing devices are configured to determine gap weights using the weight signals, wherein the gap weights are indicative of a total weight measured by the weighing device in gaps between the weight change events.

10) A system according to claim 9, wherein the one or more processing devices are configured to subtract consecutive gap weights to determine the weight for axles entering or exiting the weighing device between the consecutive gap weights.

11) A system according to any one of the claims 1 to 10, wherein the one or more processing devices are configured to identify: a) group entry events, wherein each group entry event is a group of entry events indicative of a group of axles entering the weighing device; and, b) group exit events, wherein each group exit event is a group of exit events indicative of groups of axles exiting the weighing device.

12) A system according to claim 11, wherein the one or more processing devices are configured to determine the weight of a group of axles using at least one of the group entry events and the group exit events for the group of axles. 13)A system according to claim 12, wherein the one or more processing devices are configured to subtract gap weights before and after group entry or group exit events for a group of axles to determine the weight for the group of axles entering or exiting the weighing device.

14) A system according to claim 12 or claim 13, wherein the one or more processing devices are configured to analyse the group entry events and the group exit events independently.

15) A system according to any one of the claims 1 to 14, wherein the one or more processing devices are configured to: a) determine one or more matched events, wherein the one or more matched events are entry events for an axle that correspond to exit events for the same axle; and, b) determine the weight of at least one group of axles at least partially by analysing the one or more matched group events simultaneously.

16) A system according to any one of the claims 1 to 15, wherein the one or more processing devices arc configured to: a) determine one or more matched group events, wherein the one or more matched group events are group entry events for a group of axles that correspond to group exit events for the same group of axles; and, b) determine the weight of the group of axles at least partially by analysing the one or more matched group events simultaneously.

17) A system according to claim 16, wherein the one or more processing devices are configured to determine the weight of multiple groups of axles at least partially by solving a set of linear equations based on multiple matched group events.

18) A system according to claim 17, wherein the linear equations are overdetermined linear equations.

19) A system according to claim 18, wherein the linear equations are solved in a forward and a reverse direction independently.

20) A system according to any one of the claims 16 to 19, wherein the one or more processing devices are configured to determine the weight of the at least one group of axles at least partially by solving a least squares formulation based on the one or more matched group events. 21) A system according to any one of the claims 1 to 20, wherein the one or more processing devices are configured to detect if there is a mismatch between at least one of: a) a number of entry events and number of exit events; b) a number of group entry events and number of group exit events; c) a weight of matched entry and exit events; d) a weight of matched group entry events and group exit events; e) a total weight of entry events and a total weight of exit events; and, f) a total weight of group entry events and total weight of group exit events.

22) A system according to claim 21, wherein the one or more processing devices are configured to display an error to a user through a user interface if a mismatch or abnormal weight is detected.

23) A system according to any one of the claims 1 to 22, wherein the one or more processing devices arc configured to provide an output to a user indicative of at least one of: a) a weight of at least one group of axles; and, b) a vehicle weight.

24) A system according to any one of the claims 1 to 23, wherein the weighing device is at least as long as a group of axles.

25) A system according to any one of the claims 1 to 24, wherein the vehicle traverses the weighing device substantially continuously.

26) A method for measuring the weight of one or more groups of axles, wherein the method includes one or more processing devices: a) receiving weight signals from a weighing device configured to measure a weight of a vehicle traversing the weighing device; b) analysing the weight signals to detect one or more weight change events; c) using the one or more weight change events to identify one or more entry events, wherein the one or more entry events are where one or more axles enter the weighing device; d) using the one or more weight change events to identify one or more exit events, wherein the one or more exit events are where the one or more axles exit the weighing device; e) identifying groups of axles at least partially based on a timing of the one or more entry events or the one or more exit events; and, f) using the weight signals and the identified groups of axles to determine a weight for the one or more groups of axles.