Improvements in or relating to the sustainability of pressurised fluid systems

Abstract

A controller for controlling a pressurised fluid system comprising a plurality of pressurised fluid elements and a common pressurised fluid source, the common pressurised fluid source configured to supply pressurised fluid to the plurality of pressurised fluid elements, wherein the controller is configured to, responsive to an abnormal operating state of at least one pressurised fluid element of the plurality of pressurised fluid elements, adjust a pressurised fluid operation of at least one other pressurised fluid element of the plurality of pressurised fluid elements.

Description

[0001] IMPROVEMENTS IN OR RELATING TO THE SUSTAINABILITY OF PRESSURISED FLUID SYSTEMS

[0002] Technical Field

[0003] The present disclosure relates to a controller for controlling a pressurised fluid system, such as a hydraulic or pneumatic (e.g., compressed air) system. Associated pressurised fluid systems, apparatus, methods, and corresponding computer programs are also disclosed.

[0004] Background

[0005] The real-time efficiency of a pressurised fluid system is an important factor in the development of sustainable operations and processes in areas including industry. Accordingly, there is a need for advances in pressurised fluid system control that minimise the negative performance, environmental, and / or financial impact of real-time sources of inefficiency in a pressurised fluid system.

[0006] Summary

[0007] According to a first aspect of the present disclosure, there is provided a controller for controlling a pressurised fluid system comprising a plurality of pressurised fluid elements and a common pressurised fluid source, the common pressurised fluid source configured to supply pressurised fluid to the plurality of pressurised fluid elements, wherein the controller is configured to, responsive to an abnormal operating state of at least one pressurised fluid element of the plurality of pressurised fluid elements, adjust a pressurised fluid operation of at least one other pressurised fluid element of the plurality of pressurised fluid elements.

[0008] The at least one pressurised fluid element may include at least one pressurised fluid actuator.

[0009] The at least one other pressurised fluid element may include at least one pressurised fluid actuator, at least one pressure regulator, at least one flow regulator, at least one control valve and / or at least one humidity regulator. The abnormal operating state may include or correspond to a change in individual cycle time of the at least one pressurised fluid element.

[0010] The abnormal operating state may include or correspond to a change in total cycle time of the plurality of pressurised fluid elements.

[0011] The abnormal operating state may include or correspond to a change in pressure level of the at least one pressurised fluid element.

[0012] The change in pressure level may be caused by a leak or an obstruction in the pressurised fluid system.

[0013] The abnormal operating state may include or correspond to a change in flow rate level of the at least one pressurised fluid element.

[0014] The abnormal operating state may include or correspond to a malfunction of the at least one pressurised fluid element.

[0015] Adjusting a pressurised fluid operation may include adjusting a pressure level of the at least one other pressurised fluid element.

[0016] Adjusting a pressurised fluid operation may include adjusting a flow rate level of the at least one other pressurised fluid element.

[0017] Adjusting a pressurised fluid operation may include adjusting an individual cycle time of the at least one pressurised fluid element.

[0018] The controller may be configured to, responsive to the abnormal operating state of at least one pressurised fluid element of the plurality of pressurised fluid elements: determine a plurality of possible configurations of the plurality of pressurised fluid elements; select a configuration from the plurality of possible configurations; and adjust the pressurised fluid operation of the at least one other pressurised fluid element in accordance with the selected configuration of the plurality of pressurised fluid elements. The controller may be configured to use an intelligent optimisation algorithm to select the configuration from the plurality of possible configurations. Non-limiting examples of such an intelligent optimisation algorithm are described throughout the specification.

[0019] According to a second aspect of the present disclosure, there is provided a controller for controlling a pressurised fluid system comprising a plurality of pressurised fluid elements and a common pressurised fluid source, the common pressurised fluid source configured to supply pressurised fluid to the plurality of pressurised fluid elements, wherein the controller is configured to: determine a plurality of possible configurations of the plurality of pressurised fluid elements; use an intelligent optimisation algorithm to select a configuration from the plurality of possible configurations; and configure the plurality of pressurised fluid elements in accordance with the selected configuration of the plurality of pressurised fluid elements.

[0020] The controller may be configured to select the configuration from the plurality of possible configurations in accordance with an objective function corresponding to a model of the pressurised fluid system.

[0021] The controller may be configured to evaluate each of the plurality of possible configurations to determine at least one parameter of the plurality of pressurised fluid elements.

[0022] The at least one parameter of the plurality of pressurised fluid elements may include a total cycle time of the plurality of pressurised fluid elements and / or a pressurised fluid consumption of the plurality of pressurised fluid elements.

[0023] The controller may be configured to use a fitness function to evaluate each of the plurality of possible configurations to determine at least one parameter of the plurality of pressurised fluid elements.

[0024] The controller may be configured to select the configuration from the plurality of possible configurations based on at least one target parameter of at least one pressurised fluid element of the plurality of pressurised fluid elements and / or at least one target parameter of the plurality of pressurised fluid elements. The at least one target parameter may include: a target individual cycle time of at least one pressurised fluid element of the plurality of pressurised fluid elements and / or a target total cycle time of the plurality of pressurised fluid elements; and / or a target pressurised fluid consumption of at least one pressurised fluid element of the plurality of pressurised fluid elements and / or a target pressurised fluid consumption of the plurality of pressurised fluid elements.

[0025] The intelligent optimisation algorithm may be a swarm optimisation algorithm.

[0026] The pressurised fluid system may be a pneumatic system, the plurality of pressurised fluid elements may be pneumatic elements, and the common pressurised fluid source may be a pneumatic source configured to supply compressed gas to the plurality of pneumatic elements.

[0027] According to a third aspect of the present disclosure, there is provided a pressurised fluid system comprising a plurality of pressurised fluid elements, a common pressurised fluid source and a controller, the common pressurised fluid source configured to supply compressed fluid to the plurality of pressurised fluid elements, wherein the controller is in accordance with any one of the preceding aspects.

[0028] The pressurised fluid system may include at least one monitoring device, wherein the or each monitoring device is configured to monitor an operating state of the at least one pressurised fluid element, optionally wherein the or each monitoring device is configured to monitor a cycle time, a pressure level and / or a flow rate level of the at least one pressurised fluid element.

[0029] According to a fourth aspect of the present disclosure, there is provided a method comprising: responsive to an abnormal operating state of at least one pressurised fluid element of a plurality of pressurised fluid elements, adjust a pressurised fluid operation of at least one other pressurised fluid element of the plurality of pressurised fluid elements.

[0030] According to a fifth aspect of the present disclosure, there is provided a method comprising: determining a plurality of possible configurations of a plurality of pressurised fluid elements, using an intelligent optimisation algorithm to select a configuration from the plurality of possible configurations; and configuring the plurality of pressurised fluid elements in accordance with the selected configuration of the plurality of pressurised fluid elements. According to a sixth aspect, there is provided an apparatus comprising a processor and memory including computer program code, the memory and computer program code configured to, with the processor, enable the apparatus to at least perform the method of the fourth and / or fifth aspects. The apparatus may comprise one or more of a monitoring device configured to monitor a pressurised fluid element, and an adjusting device configured to adjust a pressurised fluid element.

[0031] According to a seventh aspect, there is provided a system comprising a plurality of computers and / or data processing devices, such as a server and one or more edge devices, wherein computer resources to at least to perform the method of the fourth and / or fifth aspects are shared between or among the plurality of computers and / or data processing devices.

[0032] According to an eighth aspect, there is provided an apparatus as substantially described herein with reference to, and as illustrated by, the accompanying drawings.

[0033] The optional features described in relation to the first aspect are also applicable to the second aspect, the third aspect, the fourth aspect, the fifth aspect, the sixth aspect, the seventh aspect, and / or the eighth aspect where compatible.

[0034] The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated or understood by the skilled person.

[0035] Corresponding computer programs for implementing one or more steps of the methods disclosed herein are also within the present disclosure and are encompassed by one or more of the described examples. Accordingly, one or more methods disclosed herein may be computer-implemented.

[0036] One or more of the computer programs may, when run on a computer, cause the computer to configure any apparatus, including a battery, circuit, controller, or device disclosed herein or perform any method disclosed herein. One or more of the computer programs may be software implementations, and the computer may be considered as any appropriate hardware, including a digital signal processor, a microcontroller, and an implementation in read only memory (ROM), erasable programmable read only memory (EPROM) or electronically erasable programmable read only memory (EEPROM), as nonlimiting examples. The software may be an assembly program. One or more of the computer programs may be provided on a computer readable medium, which may be a physical computer readable medium such as a disc or a memory device, or may be embodied as a transient signal. Such a transient signal may be a network download, including an internet download.

[0037] The present disclosure includes one or more corresponding aspects, examples or features in isolation or in various combinations whether or not specifically stated (including claimed) in that combination or in isolation. Corresponding means for performing one or more of the discussed functions are also within the present disclosure.

[0038] Throughout the present specification, descriptors relating to movement or displacement such as "extend" or "retract", as well as any adjective and adverb derivatives thereof, are used in the sense of the movement of features relating to those presented in the drawings. However, such descriptors are not intended to be in any way limiting to an intended use of the described or claimed invention.

[0039] The above summary is intended to be merely exemplary and non-limiting.

[0040] It will be appreciated that the use of the terms "first" and "second", and the like, in this patent specification is merely intended to help distinguish between similar features and is not intended to indicate the relative importance of one feature over another feature, unless otherwise specified.

[0041] Within the scope of this patent application it is expressly intended that the various aspects, embodiments, examples and alternatives set out in the preceding paragraphs, and the claims and / or the following description and drawings, and in particular the individual features thereof, may be taken independently or in any combination. That is, all embodiments and all features of any embodiment can be combined in any way and / or combination, unless such features are incompatible. The applicant reserves the right to change any originally filed claim or file any new claim accordingly, including the right to amend any originally filed claim to depend from and / or incorporate any feature of any other claim although not originally claimed in that manner.

[0042] Preferred embodiments of the invention will now be described, by way of non-limiting examples, with reference to the accompanying drawings in which:

[0043] Figure 1 shows in schematic form a pneumatic system; Figure 2 shows in schematic form a pneumatic system according to an example of the disclosure;

[0044] Figure 3 shows in schematic form a controller according to an example of the disclosure;

[0045] Figure 4 shows in schematic form a pneumatic system according to another example of the disclosure;

[0046] Figure 5 shows in schematic form a pneumatic system according to another example of the disclosure;

[0047] Figure 6 shows in overview form an intelligent pneumatic system according to another example of the disclosure;

[0048] Figure 7 shows the pneumatic system of Figure 5 in a first scenario;

[0049] Figure 8 shows the pneumatic system of Figure 5 in a second scenario;

[0050] Figure 9 shows an overview of an Intelligent Optimisation such as a Swarm Optimisation algorithm process according to an example of the disclosure;

[0051] Figure 10 shows an example mathematical model of an example pneumatic element for the swarm optimisation algorithm process of Figure 9;

[0052] Figure 11 shows an example mathematical model of an example pneumatic system for the swarm optimisation algorithm process of Figure 9;

[0053] Figure 12 shows in schematic form a pneumatic system according to an example of the disclosure;

[0054] Figure 13 shows a flow diagram for a process for controlling a pneumatic system by a controller according to an example of the disclosure; and

[0055] Figure 14 shows a flow diagram for another process for controlling a pneumatic system by a controller according to an example of the disclosure.

[0056] Detailed Description

[0057] Figure 1 shows in schematic form a pneumatic system 100 comprising a plurality of pneumatic elements lOla-c (in this example a valve 101a, a solenoid valve block 101b, and a pneumatic cylinder 101c), a common pneumatic source 102 (e.g., a compressor), a storage tank 103, and an optimisation device 104.

[0058] The plurality of pneumatic elements lOla-c and the optimisation device 104 may define a machine of the pneumatic system 100 - see the dashed box - which as indicated by the reference sign 105 is experiencing a compressed gas inefficiency that lowers the overall efficiency (as shown in the inset graph) of the pneumatic system 100. Examples of the present disclosure relate to an intelligent system to autonomously monitor and control gas and energy consumption of pressurised fluid systems, pneumatic systems such as industrial compressed air systems (CAS), compressed argon, carbon dioxide, helium, nitrogen, oxygen, propane, or refrigerant gases, etc., or hydraulic systems, that, in some cases, uses an artificial intelligence-based (Al-based) approach to improve the sustainability of pressurised fluid systems by identifying sources of inefficiency (such as leakages, faults, malfunctioning actuators, pressure drops, and so forth), as well as the optimal control strategy to minimise their environmental, financial and productivity (through cycle time restoration, as discussed below) impacts.

[0059] Figure 2 shows in schematic form a pneumatic system 200 according to an example of the disclosure. Like the pneumatic system shown schematically in Figure 1, the pneumatic system 200 comprises a plurality of pneumatic elements 201a-e (e.g., a solenoid valve block 201a, and pneumatic cylinders 201b-c), a common pneumatic source 202, and a storage tank 203. Unlike the pneumatic system shown schematically in Figure 1, the pneumatic system 200 advantageously further comprises controllers 206 collectively configured to continuously monitor the performance of the pneumatic system 200 and preferably use Intelligent Optimisation such as Swarm Optimisation, Al, and / or Industrial Internet of Things (IIoT) to identify, locate, distinguish, and / or classify inefficiency sources 205 on the demand side of the pneumatic system 200 in real-time. The swarm of intelligent controllers 206 operates at system level to control several pneumatic elements 201a-e concurrently to maintain a relatively high overall efficiency of the pneumatic system 200 (compare the inset graph of Figure 2 with the inset graph of Figure 1).

[0060] As with Figure 1, dashed boxes (and the pneumatic elements and controllers therein) may define machines of the pneumatic system 200.

[0061] The controllers 206 may have or may realise one or more of the following advantages:

[0062] • Targeting the whole pneumatic system using multiple devices rather than individual machines.

[0063] Rather than applying monitoring and control of a single pneumatic element (a term that, for purpose of the present disclosure, encompasses a pneumatic component / device / machine, and / or a pneumatic piece of equipment), the controllers 206 collect data from different pneumatic elements (i.e., different components, devices, machines and equipment etc.) which are connected to a common pneumatic source. This allows the controllers 206 to deploy control strategies which may act beyond the scope of the equipment (i.e., in different circuits) where the fault was detected but will also include other equipment which may be operating close-by and making use of the same common pneumatic source.

[0064] • Targeting the demand side of the pneumatic system.

[0065] The controllers 206 may apply control strategies and / or adjustments within the demand (use) side of the pneumatic system. They may directly minimise the amount (volume) of compressed gas used by the equipment, rather than controlling the compressors supplying the compressed gas.

[0066] • Identifies and locates inefficiencies and leakages without the need for additional gas flow sensors.

[0067] Whilst gas flow sensors facilitate detection of leaks (a leak generates an increased gas flow and increases the volume of gas required to operate the equipment), the accuracy and uncertainty in gas flow measurement plays an increased role, especially in larger pneumatic systems.

[0068] Hence, smaller leaks and faults on the demand-side will most likely go undetected. Furthermore, it may be very difficult to identify the location of a fault, unless multiple gas flow sensors are added to each piece of equipment, making the cost of this prohibitive. The controllers 206 may therefore use pressure and / or actuator cycle time data to detect and locate the presence of faults. Various data pre-processing techniques may be utilised to generate and monitor metrics (such as mean, variance, standard deviation, and other statistics), and their change over time is used to detect and locate the presence of faults within the system.

[0069] • Smart and integrated controllers 206 can be installed on existing compressed gas networks, without the need for proprietary devices.

[0070] The data / metrics that may be utilised (pressure and / or cycle time) are already being measured in a majority of industrial systems which make use of compressed gas; hence no additional equipment may be required for fault detection. Furthermore, if any sensors (more generally, monitoring devices) need to be installed, these may be generic sensors, such that no proprietary / customised equipment is utilised. Data may be collected directly from the programmable logic controllers (PLCs) of the equipment, especially if these are Industry 4.0 ready; i.e., they allow for external communication via loT protocols such as MQTT (originally an initialism of MQ Telemetry Transport) and Open Platform Communications / Unified Architecture (OPC / UA) to the controllers 206. These controllers 206 may be edge-devices which collect and process the data at source but could also communicate this data (or some data) to the cloud (local or remote) for further processing. Hence, resources for processing in accordance with examples herein may be shared between or among a plurality of computing devices, such as a server and one or more edge devices, computers or data processing devices.

[0071] • Using Al to identify inefficiencies.

[0072] Data collected from the network of controllers 206 may be analysed using Al / machine learning (ML) techniques such as classification (k-nearest neighbours, KNN, and support vector machine, SVM) and neural networks to identify, classify and locate faults according to their data fingerprint.

[0073] • Using Al to monitor and control the pneumatic system 200 holistically rather than having several stand-alone devices.

[0074] Multiple controllers 206 may be used to holistically generate a model of the as-is state of the pneumatic system 200, and the various pneumatic elements / components / machines / equipment / system / sub-system that uses a common supply, including any faults and their location. This allows for the possibility of generating a range of possible to-be solutions (possible to-be configurations), which make up a solution (configuration) space, comprising possible control options which holistically encompass all the equipment within the pneumatic system network. For example, this could mean that the pressure (hence gas flow) of one machine / subsystem could be reduced whilst another machine's pressure control would be increased to make up for the cycle time lost.

[0075] • Using Intelligent Optimisation such as Swarm Optimisation to perform the required control actions.

[0076] The range of possible solutions (or models or configurations) for the pneumatic system may be analysed using an objective function to generate a solution space based on a set of control options (e.g. pressure reduction / increase at various locations). Intelligent Optimisation such as Swarm Optimisation techniques may be used to traverse and identify the ideal solution within this solution space. The controllers 206 may then autonomously implement such solutions in real-time based on a set of rules, or else recommend these to a maintenance / operations team who may then decide whether to opt for such a solution.

[0077] The controllers 206 are representative of the following examples in which the reader will recognise that the underlying principles are applicable to pressurised fluid systems generally.

[0078] Figure 3 shows in schematic form a controller 306 according to an example of the disclosure. The controller 306 is for controlling a pressurised fluid system comprising a plurality of pressurised fluid elements and a common pressurised fluid source, the common pneumatic source configured to supply pressurised fluid to the plurality of pressurised fluid elements, such as those described with reference to Figure 2.

[0079] The controller 306 is configured to, responsive to an abnormal operating state of at least one pressurised fluid element of the plurality of pressurised fluid elements, adjust a pressurised fluid operation of at least one other pressurised fluid element of the plurality of pressurised fluid elements, and thereby realise at least one of the advantages described above.

[0080] The controller 306 may be implemented through hardware or a combination of hardware and software. For example, the controller 306 may be implemented as a PLC or programmable controller (or other type of industrial computer) with a device(s), module(s), and / or unit(s) configured to adjust a pneumatic operation of a pneumatic element.

[0081] In some examples, the pressurised fluid system is a pneumatic system, the plurality of pressurised fluid elements are pneumatic elements, and the common pressurised fluid source is a pneumatic source configured to supply compressed gas to the plurality of pneumatic elements. For instance, referring again to Figure 2 and its discussion, it will be appreciated that the at least one pressurised fluid element may include any type of pneumatic component(s) and / or machine(s) (and / or device(s)), such as at least one pneumatic actuator, and that the at least one other pneumatic element may also include any type of pneumatic component and / or machine (and / or device), including a pneumatic actuator, a pneumatic cylinder, a pressure regulator, a flow regulator and / or a control valve. Alternatively, the pressurised fluid system may be a hydraulic system, the reader appreciating that the terminology used herein should be updated accordingly (e.g., to a hydraulic element, a hydraulic source, a hydraulic actuator etc.). Further examples of pneumatic systems, data collection, and control

[0082] Figure 4 shows in schematic form a pneumatic system 400 according to another example of the disclosure. In this example, the pneumatic system 400 comprises four zones: a supply-side zone 407 (Zone 0) and three demand-side zones 408a-c (Zones A-C). Zone 0 includes a compressor 402, a gas tank 403 and a plant consumer unit 409 that collectively supply compressed gas to: the consumer unit 410 of Zone A; the valve manifold 401a and solenoid valve 401b of Zone B; and, through the extension and retraction lines 411, 412 and the flow controller 401c of Zone C, the pneumatic cylinder 401d of Zone C. Zone C also includes proximity switches 413 for detecting the position of the pneumatic cylinder during its operation, proximity switch signals 414 to or from which are sent from or received by one or more data monitoring devices or a processing unit such as a programmable logic controller 415.

[0083] The sides and zones of the pneumatic system 400 may also be described as set out in Table 1.

[0084] Table 1

[0085] Figure 5 shows in schematic form a pneumatic system 500 according to another example of the disclosure, more specifically a pneumatic system comprising 'm' machines, which comprise 'n' stations each. Although like reference signs to Figure 4 have been omitted for clarity, it will be appreciated that each station includes valve block(s), their solenoid valve(s) controlling the supply of compressed gas to the station's multiple actuators. Some actuator examples include double-acting cylinders, grippers, vacuum equipment, etc. Each actuator is equipped with pneumatic controller(s) such as proportional flow regulator(s), proportional pressure regulator(s), etc. Zone 0 has a data collection point, DCP, 516. The other Zones also have DCPs 516 in addition to control points, CPs, 517.

[0086] In some examples, DCP & CPs within Zones A-C may be required. However, in Zone C, CPs may not be mandatory for each actuator.

[0087] Through DCPs in Zone A and / or Zone B and / or Zone C, measurements such as flow rate (flow sensor), pressure (pressure transducer), temperature (temperature sensor), humidity (humidity sensor), cycle time (via actuator proximity switches), among others may be recorded. These three zones may be used as potential CPs for pneumatic control equipment such as pressure and flow proportional regulator(s), control valves, etc.

[0088] Additionally, total cycle time (TCT) is the time taken to complete one entire actuation sequence cycle (time to complete all the actuations), while individual cycle time (ICT) is the time taken for each actuation (extend / retract / supply fluid / vacuum fluid) by each actuator.

[0089] Figure 6 shows in overview form an intelligent pneumatic system 600 according to another example of the disclosure. The intelligent pneumatic system 600 includes six subsystems 620-626 that operate as follows.

[0090] 1. Monitoring System

[0091] The monitoring system 620 continuously gathers data through the selected DCPs. Thus, datasets of compressed gas parameters are continuously generated.

[0092] 2. Data Transmission System

[0093] These datasets are transferred into a database system via the data transmission system 621, using communication protocol(s) and / or edge device(s) and / or cloud computing service(s), etc.

[0094] 3. Intelligent Data Processing System

[0095] Data processing and intelligent analyses are continuously performed by the intelligent data processing system 622. Fault detection indicators such as average, standard deviation, impulse factor, and / or others of pneumatic data, are calculated using preprocessing techniques. In this stage, data warehousing of the processed datasets is also carried out. Intelligent analysis is then carried out on these indicators using classification methods, such as kNN, k-means, SVM, NNs, and / or others.

[0096] 4. Intelligent Optimisation and Decision System

[0097] As part of the intelligent optimisation technique, a range of possible control strategies are generated by and to-be potential solutions are modelled by the intelligent optimisation and decision system 623, using techniques such as brute force and / or evolutionary and / or genetic algorithms etc. The intelligent optimisation will then use techniques, such as swarm optimisation, to identify the best control strategy based on defined control rules. This is because, as per the control strategies, the combinations and permutations of possible solutions generates a large solution space which is difficult for humans to consider in the decision-making process. Adjustment(s) would be performed in such a way that any present fault(s) consume less compressed gas, whilst maintaining the system performance close to the benchmark as possible, to reduce the difference A in ICT and TCT. This could be implemented by carrying out adjustment(s) to the pneumatic controllers.

[0098] 5. Human Machine Interface (HMI)

[0099] The HMI system 624 is integrated throughout all stages and subsystems. The pneumatic system parameters are monitored on an HMI by using the data collected from the DCPs within the monitoring system 620. This is possible through the transfer and / or request of data to and / or from the other elements within the data transmission system 621, such as the edge device(s), cloud service(s), etc. Hence, general analytics are made available to a user. Visualising fault detection and / or prediction, etc., is accessible on the developed HMI, such as via classification plots, etc. Additionally, the HMI provides the solution(s) found by the intelligent optimisation system to a user or maintenance / operations team. The system may then request human intervention to select and / or approve the control solution for deployment, to the pneumatic controllers.

[0100] 6. Control System

[0101] Based on the optimisation and / or decision-making performed in the previous stages, control action(s) are deployed automatically by the control system 625 to the pneumatic control equipment in the required zone(s) and / or circuits. The consequences of the deployed control action(s) are monitored and analysed through the feedback loop of the subsystems. This means that the new behaviour of the pneumatic system after deploying the control action(s) is monitored. This is carried out through the data collection of new compressed gas datasets and their transmission, along with the data processing and intelligent analyses.

[0102] Hence, the intelligent pneumatic system 600 may include a closed loop comprising; (i) data generation, (ii) transmission, (iii) intelligent data processing, (iv) intelligent optimisation and decision making, and (v) control actions.

[0103] Example initial setup

[0104] An initial setup procedure may be performed when first installing the controllers on a long-standing and / or newly installed pneumatic system. This procedure may be required if modelling of the pneumatic system is to take place, e.g., for the consequential intelligent data analysis, optimisation, and control strategy deployment. This procedure can also be performed even after installation, for instance for calibration purposes etc.

[0105] An initial setting up procedure may include the following steps:

[0106] • The monitoring system 620 initiates the gathering of data through the selected data collection points. Thus, datasets of compressed gas parameters are generated.

[0107] • These datasets are transferred into a database system via the data transmission system setup 621, using communication protocol(s) and / or edge device(s) and / or cloud computing services, etc.

[0108] • The intelligent data and processing system 622 models the demand-side of the intelligent pneumatic system 600, such as stations and / or subsystems and / or machines and / or actuators, etc. Fault detection indicators such as average, standard deviation, impulse factor, and / or others, are calculated using preprocessing techniques, which may be required for the consequent Al and ML algorithm(s) to detect and / or predict any faults and / or optimise the system.

[0109] The as-is system modelling procedure is carried out during the initial setup and potentially, at one or more times thereafter to maintain system effectiveness. For example, further modelling may be carried out after a stipulated timeframe, for example daily and / or weekly and / or monthly and / or annually, etc. Example scenarios

[0110] Three typical scenarios in which examples of the disclosure may be applied will now be described. The first scenario is defined as a benchmark scenario, as it deals with a situation where there are no faults in a typical pneumatic system, and thus, no intelligent decision and control actions are required. Additionally, the other two scenarios comprise typical faults for which controllers may identify the presence of said faults and make intelligent decisions to deploy the ideal control strategy. The two typical faults presented are a leak and a pressure drop within Zone C, which are the most common types of faults detected.

[0111] It will be appreciated that the scenarios described herein are exemplary of an abnormal operating state including or corresponding to a change in individual cycle time of the at least one pneumatic element, a change in total cycle time of the plurality of pneumatic elements, a change in pressure level of the at least one pneumatic element, which may be caused by a leak or an obstruction in the pneumatic system, a change in flow rate level of the at least one pneumatic element, and / or a malfunction of the at least one pneumatic element. These and related aspects will become apparent from the following discussion.

[0112] In this scenario, no fault(s) (abnormal operating state(s)) are present. Thus, this scenario can be referred to as the benchmark scenario.

[0113] 1. Monitoring System

[0114] The monitoring system 620 is continuously gathering data through the selected data collection points. In this scenario, no faults are present, yet still datasets of compressed gas parameters are continuously generated.

[0115] 2. Data Transmission System

[0116] These datasets are transferred into a database system via the data transmission system 621, using communication protocol(s) and / or edge device(s) and / or cloud computing service(s), etc.

[0117] 3. Intelligent Data Processing System Data processing and intelligent analyses are continuously performed by the intelligent optimisation and decision system 623. Fault detection indicators such as average, standard deviation, impulse factor, and / or others, are calculated using pre-processing techniques. In this stage, data warehousing of the processed datasets is also carried out. Intelligent analysis is then carried out on these indicators using classification methods, such as kNN, k-means, SVM, NNs, and / or others.

[0118] In this scenario, this intelligent analysis determines that the pneumatic system is not faulty.

[0119] 4. Intelligent Optimisation and Decision System

[0120] Since the system does not have any faults, no intelligent decisions through optimisation are required.

[0121] 5. Human Machine Interface (HMI)

[0122] The HMI system 624 is integrated throughout all stages and subsystems. The pneumatic system parameters are monitored on the HMI 624 by using the data collected within the monitoring system 620, via the selected communication protocol within the data transmission system 621. This is also possible through the transfer and / or request of data to and / or from the other elements within the data transmission system, such as the edge device(s), cloud service(s), etc. Hence, general analytics are made available to the user.

[0123] 6. Control System

[0124] As no optimisation or decision-making was performed, then no control actions are required from the respective control points.

[0125] Scenario 1 : Leak in Zone C

[0126] Figure 7 shows the pneumatic system 500 of Figure 5 in a scenario in which an abnormal operating state, specifically a leak 505a, is occurring in Zone C. Reference signs for the existing features of the pneumatic system 500 have been omitted for clarity. A leak is when compressed gas escapes from the pneumatic system 500 unwantedly. This can be a result of a faulty actuator seal, a loose fitting in the distribution piping, etc. When leak(s) are present in actuator(s) in Station 1 within Zone C, the pressure at DCP(s) Cl and / or C3 decreases. Consequently, the flow rate at DCP(s) Cl and / or C3 increases.

[0127] Due to the leak(s), the ICT of the effected actuator(s) experiences a change. Thus, TCT of the effected station(s) changes.

[0128] In Zones A and B, i.e. at any of the DCP(s) Al, A2, Bl, B2, and / or B3, etc., a drop in pressure is experienced, together with an increase in flow rate.

[0129] A scenario summary is therefore: pressure = J.A; flow rate = fA; ICT = A; and TCT = A.

[0130] 1. Monitoring System

[0131] The monitoring system 620 is continuously gathering data through the selected DCPs. In this scenario, leak(s) within station(s) in Zone C are present and the datasets of compressed gas parameters are continuously generated.

[0132] 2. Data Transmission System

[0133] These datasets are transferred into a database system via the data transmission system 621, using communication protocol(s) and / or edge device(s) and / or cloud computing service(s), etc.

[0134] 3. Intelligent Data Processing System

[0135] Data processing and intelligent analyses are continuously performed by the by the intelligent data processing system 622. Fault detection indicators such as average, standard deviation, impulse factor, and / or others, are calculated using pre-processing techniques. In this stage, data warehousing of the processed datasets is also carried out. Intelligent analysis is then performed on these indicators using classification methods, such as kNN, k-means, SVM, NNs, and / or others.

[0136] This intelligent data analysis results in the (i) detection, (ii) localisation, and (iii) severity of the leak(s) in Zone C.

[0137] 4. Intelligent Optimisation and Decision System As part of the intelligent optimisation technique, a range of possible control strategies are generated and to-be potential solutions are modelled by the intelligent optimisation and decision system 623, using techniques such as brute force and / or evolutionary and / or genetic algorithms etc. The intelligent optimisation will then use techniques, such as swarm optimisation, to identify the best control strategy based on defined control rules. Adjustment(s) would be performed in such a way that Zone C leak(s) consume less compressed gas, whilst maintaining the system performance close to the benchmark as possible, to reduce the A in ICT and TCT. These adjustment(s) could be implemented by changing the settings of the pneumatic controllers, such as the proportional pressure regulator(s) and / or proportional flow regulator(s), as per the following possible control strategies: i. Direct control from CP(s) at C2 and / or C4, etc. connected to leaking actuator(s)

[0138] This control strategy involves the adjustment(s) of multiple pneumatic controllers in CP(s) at C2 and / or C4, etc. :

[0139] Adjustment(s) by proportional pressure regulator(s) at CP(s) C2 and / or C4, etc. are performed in such a way that the pressure for the effected actuator(s) is reduced.

[0140] To compensate for this pressure change, adjustment(s) by the proportional flow regulator(s) at CP(s) C2 and / or C4, etc. are performed to increase the flow rate through the effected actuator(s).

[0141] Result: This maintains the ICT and TCT of the effected actuator(s) and station(s), respectively, as close to the benchmark as possible, while reducing the compressed gas consumption of the leak(s) in Zone C. ii. Indirect control from CP(s) at Bl and / or B2 and / or B3, etc.

[0142] This control strategy involves the adjustment(s) of multiple pneumatic controllers in CP(s) at Bl and / or B2 and / or B3, etc. :

[0143] Adjustment(s) by the proportional pressure regulator(s) at CP Bl are performed in such a way that the pressure in Zone C is reduced, together with the effected stations(s) and actuator(s). To compensate for this pressure change, adjustment(s) by the proportional flow regulator(s) at CP Bl are performed to increase the flow rate in Zone C, together with the effected stations(s) and actuator(s).

[0144] Adjustment(s) at CP(s) B2 and / or B3, etc. for both pressure and flow rate may be performed to maintain the performance of their station(s) and machine(s), with respect to their ICT and TCT, while reducing the CA consumption of the leak(s) in Zone C.

[0145] Result: This maintains the ICT and TCT of the effected actuator(s) and station(s), respectively, as close to the benchmark as possible.

[0146] Hi. Indirect control from CP(s) at Al and / or A2, etc.

[0147] This control strategy involves the adjustment(s) of multiple pneumatic controllers in CP(s) at Al and / or A2, etc. :

[0148] Adjustment(s) by the proportional pressure regulator(s) at CP Al are performed in such a way that the pressure in Zones B and C is reduced. This is carried out to reduce the pressure in the effected actuator(s) and their respective station(s).

[0149] To compensate for this pressure change, adjustment(s) by the proportional flow regulator(s) at CP Al are performed to increase the flow rate through the effected station(s) and actuator(s).

[0150] Adjustment(s) at CP A2, etc. for both pressure and flow rate may be performed to maintain the performance of their station(s) and machine(s), with respect to their ICT and TCT.

[0151] Result: This maintains the ICT and TCT of the effected actuator(s) and station(s), respectively, as close to the benchmark as possible. iv. Combination of direct and indirect control

[0152] There is also the possibility to perform both direct and indirect control action(s) in conjunction with each other. Hence, adjustment(s) of the pneumatic controller(s) may be performed on the effected actuator(s), whilst simultaneously performing indirect adjustment(s) of the pneumatic controller(s) in the other actuator(s) and / or station(s) and / or machine(s) and / or zone(s) to maintain the overall system performance. 5. Human Machine Interface (HMI)

[0153] The HMI system 624 is integrated throughout all stages and subsystems. The pneumatic system parameters are monitored on an HMI by using the data collected from the data collection points within the monitoring system 620. This is possible through the transfer and / or request of data to and / or from the other elements within the data transmission system 621, such as the edge device(s), cloud service(s), etc. General analytics are made available to the user, by means of reporting methods etc. Visualising fault detection and / or prediction, etc., is accessible on the developed HMI 624, such as via classification plots, etc. In this scenario, leak(s) occur in Zone C and the HMI 624 provides the solutions found by the intelligent optimisation and decision system 623. The system may then request human intervention to select and / or approve the control solution for deployment, to the pneumatic controllers.

[0154] 6. Control System

[0155] Based on the optimisation and / or decision-making performed in the previous stages, control action(s) are deployed automatically by the control system 625 to the pneumatic control equipment in the required zone(s) and / or circuits. The consequences of the deployed control action(s) are monitored and analysed through the feedback loop of the subsystems. This means that the new behaviour of the typical pneumatic system after deploying the control action(s) is monitored.

[0156] Scenario 2: Pressure-Drop in Zone C

[0157] Figure 8 shows the pneumatic system 500 of Figure 5 in a scenario in which a different abnormal operating state, specifically a pressure drop 505b, is occurring in Zone C. Reference signs for the existing features of the pneumatic system 500 have been omitted for clarity. A pressure-drop results in an unwanted decrease in pressure due to an obstruction in the pneumatic system 500. This can be a result of a blockage, clogged filter, pipe bend, etc.

[0158] When pressure drop(s) are present in actuator(s) in Station 1 within Zone C, the pressure at DCP(s) Cl and / or C3 decreases. Consequently, the flow rate at DCP(s) Cl and / or C3 decreases. Due to the pressure drop(s), the ICT of the effected actuator(s) increases. Thus, TCT of the effected station(s) increases as well.

[0159] Throughout Zones A and B, i.e. at DCPs Al, A2, Bl, B2, and B3, etc., no drop in pressure is experienced. However, the flow rate decreases.

[0160] A scenario summary is therefore: pressure = J.A; flow rate = J.A; ICT = fA; and TCT = fA.

[0161] 1. Monitoring System

[0162] The monitoring system 620 is continuously gathering data through the selected DCPs. In this scenario, leak(s) within station(s) in Zone C are present and the datasets of compressed gas parameters are continuously generated.

[0163] 2. Data Transmission System

[0164] These datasets are transferred into a database system via the data transmission system 621, using communication protocol(s) and / or edge device(s) and / or cloud computing service(s), etc.

[0165] 3. Intelligent Data Processing System

[0166] Data processing and intelligent analyses are continuously performed by the by the intelligent data processing system 622. Fault detection indicators such as average, standard deviation, impulse factor, and / or others, are calculated using pre-processing techniques. In this stage, data warehousing of the processed datasets is also carried out. Intelligent analysis is then performed on these indicators using classification methods, such as kNN, k-means, SVM, NNs, and / or others.

[0167] This intelligent data analysis results in the detection, localisation, and severity of the pressure drop(s) in Zone C.

[0168] 4. Intelligent Optimisation and Decision System

[0169] As part of the intelligent optimisation technique, a range of possible control strategies are generated and to-be potential solutions are modelled, using techniques such as brute force and / or evolutionary and / or genetic algorithms etc. The intelligent optimisation will then use techniques, such as swarm optimisation, to identify the best control strategy based on defined control rules. Adjustment(s) would be performed in such a way to maintain the effects of the Zone C pressure drop(s), whilst keeping the system performance close to the benchmark as possible, to reduce the A in ICT and TCT. This could be implemented by carrying out adjustment(s) to the pneumatic controllers, such as the proportional pressure regulator(s) and / or proportional flow regulator(s), as per the following possible control strategies: i. Direct control from CP(s) at C2 and / or C4, etc. connected in line with pressure drop(s)

[0170] Adjustment(s) by the proportional pressure regulator(s) at CP(s) C2 and / or C4, etc. are performed in such a way that the pressure is increased for the effected actuator(s).

[0171] As a result, to compensate for this pressure change, adjustment(s) by the proportional flow regulator(s) at CP(s) C2 and / or C4, etc. are performed to increase the flow rate through the effected actuator(s).

[0172] Result: This maintains the ICT and TCT of the effected actuator(s) and station(s), respectively, as close to the benchmark as possible. ii. Indirect control from CP(s) at Bl and / or B2 and / or B3, etc.

[0173] Adjustment(s) by the proportional pressure regulator(s) at CP Bl are performed in such a way that the pressure in Zone C is increased, together with their effected stations(s) and actuator(s)

[0174] To compensate for this pressure change, adjustment(s) by the proportional flow regulator(s) at CP Bl are performed to increase the flow rate through the effected station(s) and actuator(s).

[0175] Adjustment(s) in CP(s) B2 and / or B3, etc may be performed to decrease the pressure and flow rate in their respective stations, to maintain the compressed gas consumption close to the benchmark. Such adjustment(s) would balance the performance of the associated station(s) and machine(s) with respect to their ICT and TCT. Result: This maintains the ICT and TCT of the effected actuator(s) and station(s), respectively, as close to the benchmark as possible.

[0176] Hi. Indirect control from CP(s) at Al and / or A2, etc.

[0177] Adjustment(s) by the proportional pressure regulator(s) at CP Al are performed in such a way that the pressure in Zones B and C, together with their effected actuator(s) and their respective station(s) is increased.

[0178] To compensate for this pressure change, adjustment(s) by the proportional flow regulator(s) at CP Al are performed to increase the flow rate through the effected station(s) and actuator(s).

[0179] Adjustment(s) in CP A2, etc. for both pressure and flow rate may be performed to decrease the pressure and flow rate in their respective stations, to maintain the compressed gas consumption close to the benchmark. Such adjustment(s) would balance the performance the performance of their station(s) and machine(s), in respect to their ICT and TCT.

[0180] Result: This maintains the ICT and TCT of the effected actuator(s) and station(s), respectively, as close to the benchmark as possible. iv. Combination of direct and indirect control

[0181] There is also the possibility to perform both direct and indirect control action(s) in conjunction with each other. Hence, adjustment(s) of the pneumatic controller(s) may be performed on the effected actuator(s), whilst simultaneously performing indirect adjustment(s) of the pneumatic controller(s) in the other actuator(s) and / or station(s) and / or machine(s) and / or zone(s) to maintain the overall system performance.

[0182] 5. Human Machine Interface (HMI)

[0183] The HMI system 624 is integrated throughout all stages and subsystems. The pneumatic system parameters are monitored on an HMI by using the data collected from the data collection points within the monitoring system 620. This is possible through the transfer and / or request of data to and / or from the other elements within the data transmission system 621, such as the edge device(s), cloud service(s), etc. General analytics are made available to the user, by means of reporting methods etc. Visualising fault detection and / or prediction, etc., is accessible on the developed HMI 624, such as via classification plots, etc. Since a pressure drop(s) occur in Zone C, the HMI 624 provides the solutions found by the intelligent optimisation system 623. The system may then request human intervention to select the control solution for deployment, to the pneumatic controllers.

[0184] 6. Control System

[0185] Based on the optimisation and / or decision-making performed in the previous stages, control action(s) are deployed automatically by the control system 625 to the pneumatic control equipment in the required zone(s) and / or circuits. The consequences of the deployed control action(s) are monitored and analysed through the feedback loop of the subsystems. This means that the new behaviour of the typical pneumatic system after deploying the control action(s) is monitored.

[0186] As indicated above, the described scenarios are exemplary only. In a more general sense, adjusting a pneumatic operation may include adjusting a pressure level, a flow rate level, and / or an individual cycle time of the at least one pneumatic element. Also, responsive to the abnormal operating state of at least one pneumatic element of the plurality of pneumatic elements, the controller 206, 306, 517 may be configured to determine a plurality of possible configurations of the plurality of pneumatic elements; select a configuration from the plurality of possible configurations; and adjust the pneumatic operation of the at least one other pneumatic element in accordance with the selected configuration of the plurality of pneumatic element.

[0187] Intelligent Optimisation such as Swarm Optimisation

[0188] Once a leak, fault, pressure drop, malfunction or any kind of abnormal operating state is detected, an Intelligent Optimisation such as a Swarm Optimisation algorithm process may optimise the parameters of the pneumatic system with respect to pre-defined optimisation control rules.

[0189] Figure 9 shows in overview form an Intelligent Optimisation such as a Swarm Optimisation algorithm, SOA, process 930 according to an example of the disclosure. The process comprises a first stage (Stage 0) 931 of data collection and modelling, a second stage (Stage 1) 932 of solution generation and evaluation, and a third stage (Stage 2) 933 of optimisation. Figure 10 shows an example mathematical model 1040 of an example pneumatic element for the Intelligent Optimisation such as Swarm Optimisation algorithm process of Figure 9. Specifically, at Stage 0 of the Intelligent Optimisation Algorithm, pneumatic elements (in the example case of Figure 10, a compressed gas cylinder) are represented by a mathematical model. This mathematical model may be formulated either analytically (using fluid dynamics analysis) or empirically. This mathematical model uses known input parameters 1041, such as pressure, cycle time, and / or gas (e.g., air, helium, or nitrogen etc.) flow rate, etc., collected from the data monitoring system within the pneumatic system, or corresponding known input parameters (pressure, cycle time, and / or liquid flow rate, etc.) in the case of a hydraulic system. This example mathematical model is created by formulating relations between these parameters, to create an objective function / (%,) = yir1042, which outputs a set of parameters 1043, such as cycle time and gas (e.g., air, helium, or nitrogen etc.) consumption. For instance, an objective function may be generated based on empirical data for example using a statistical method (such as polynomial regression) to fit a polynomial equation to the data, to model the relationship between independent parameters and dependent variables.

[0190] Figure 11 shows an example mathematical model 1150 of an example pneumatic system for the Intelligent Optimisation such as Swarm Optimisation algorithm process of Figure 9. As illustrated, a pneumatic system is composed of a compressed gas network which comprises a plurality of pneumatic elements. Input parameters, such as pressure, for each element can be determined. Their range of values is dependent on a set of constraints and relationships. For example, the value of the pressure P3 is dependent on pressures P2 and Pl and cannot be greater than these values (upper constraints).

[0191] The objective function for such a network may be represented by a summation of the objective functions for each pneumatic element:

[0192] At Stage 1 of the Intelligent Optimisation Algorithm, a range of possible pneumatic element solutions (configurations) can be generated and modelled using the above- mentioned modelling approach. A set of hyper parameters (such as inertia, cognitive and social parameters to guide the particles towards better solutions) can be determined during the optimisation process. This can then be followed by an assessment of each pneumatic element solution (configuration) with the use of a fitness function, for example to determine the cycle time and gas consumption of each pneumatic element.

[0193] For example, where: y-i=Cycle Time

[0194] The result of: would be to find the minimum cycle time or minimum gas consumption of all the pneumatic elements.

[0195] This could mean, for example, that if a leak is present in a cylinder, the pressure in that branch could be reduced to minimise the leak. This would result in an increase in the cycle time of that cylinder. To make up for this reduced cycle time, pressure in the branch of (at least one other) cylinder would be increased and would result in a reduction of the respective cylinder's cycle time. This would mean that the resultant summation of cycle times for such a scenario is minimised, whilst also reducing the total gas consumption.

[0196] In a general sense, therefore, a controller may be configured to use an intelligent optimisation algorithm to select the configuration (according to which the pneumatic operation of the at least one other pneumatic element is adjusted) from the plurality of possible configurations.

[0197] In a more general sense, a controller is configured to determine a plurality of possible configurations of the plurality of pressurised fluid (e.g., pneumatic) elements; use an intelligent optimisation algorithm to select a configuration from the plurality of possible configurations; and configure the plurality of pressurised fluid elements in accordance with the selected configuration of the plurality of pressurised fluid elements.

[0198] An objective function corresponding to a model of the pneumatic system may be used to select the configuration from the plurality of possible configurations. The controller may be configured to evaluate each of the plurality of possible configurations to determine at least one parameter of the plurality of pneumatic elements, which may include a total cycle time of the plurality of pneumatic elements and / or a compressed gas consumption of the plurality of pneumatic elements. The controller may in addition (or as an alternative) be configured to use a fitness function to evaluate each of the plurality of possible configurations to determine at least one parameter of the plurality of pneumatic elements. Selecting the configuration from the plurality of possible configurations may be based on at least one target parameter of the plurality of pneumatic elements, such as a target total cycle time of the plurality of pneumatic elements and / or a target compressed gas consumption of the plurality of pneumatic elements.

[0199] Also, as set out in some of the above examples, the intelligent optimisation algorithm may be a metaheuristic type algorithm, such as a swarm optimisation algorithm, an evolutionary algorithm, and a bio-inspired algorithm.

[0200] Figure 12 shows in schematic form a pressurised fluid system 1200 according to an example of the disclosure. The pressurised fluid system comprises a plurality of pressurised fluid system elements 1201a, 1201b (...), 1201n (the precise number and arrangement of illustrated pneumatic elements is non-limiting in this respect), a common pressurised fluid system source 1202 and a controller 1206, the common pressurised fluid system source 1202 configured to supply pressurised fluid to the plurality of pressurised fluid system elements 1201a, 1201b (...), 1201n, wherein the controller 1206 is as described with reference to Figure 3 and, in example cases, Figures 2 and 5-11.

[0201] The pressurised fluid system 1200 may include at least one monitoring device 1260 configured (or each configured) to monitor an operating state (e.g., a cycle time, a pressure level and / or a flow rate level) of the at least one pressurised fluid element (i.e., one that is in an abnormal operating state, for instance the pressurised fluid element indicated with the reference sign 1201n).

[0202] Figure 13 shows a flow diagram for a process 1300 for controlling a pressurised fluid system by a controller according to an example of the disclosure. The process 1300 comprises responsive to an abnormal operating state of at least one pressurised fluid element of the plurality of pressurised fluid elements, adjusting 1370 a pressurised fluid operation of at least one other pressurised fluid element of the plurality of pressurised fluid elements.

[0203] Figure 14 shows a flow diagram for another process 1400 for controlling a pressurised fluid system by a controller according to an example of the disclosure. The process 1400 comprises determining 1480 a plurality of possible configurations of the plurality of pressurised fluid elements, using 1481 an intelligent optimisation algorithm (e.g., a swarm optimisation algorithm) to select a configuration from the plurality of possible configurations; and configuring 1482 the plurality of pressurised fluid elements in accordance with the selected configuration of the plurality of pressurised fluid elements.

[0204] It will be appreciated that the foregoing examples of the disclosure may use other types of pressurised fluid elements, pressurised fluid sources, controllers and / or monitoring devices in place of their aforementioned counterparts, without departing from the scope of the invention. In particular, where examples of the disclosure concern pneumatic systems, the underlying principles also apply to hydraulic systems.

[0205] It will also be appreciated that the above numerical values are merely intended to help illustrate the working of the invention and are not necessarily limiting on the scope of the invention.

[0206] The listing or discussion of an apparently prior-published document or apparently prior- published information in this specification should not necessarily be taken as an acknowledgement that the document or information is part of the state of the art or is common general knowledge.

[0207] Preferences and options for a given aspect, feature or parameter of the invention should, unless the context indicates otherwise, be regarded as having been disclosed in combination with any and all preferences and options for all other aspects, features and parameters of the invention. One or more aspects / examples of the present disclosure may or may not address one or more of the background issues.

Claims

CLAIMS1. A controller for controlling a pressurised fluid system comprising a plurality of pressurised fluid elements and a common pressurised fluid source, the common pressurised fluid source configured to supply pressurised fluid to the plurality of pressurised fluid elements, wherein the controller is configured to, responsive to an abnormal operating state of at least one pressurised fluid element of the plurality of pressurised fluid elements, adjust a pressurised fluid operation of at least one other pressurised fluid element of the plurality of pressurised fluid elements.

2. A controller according to Claim 1 wherein the at least one pressurised fluid element includes at least one pressurised fluid actuator.

3. A controller according to any one of the preceding claims wherein the at least one other pressurised fluid element includes at least one pressurised fluid actuator, at least one pressure regulator, at least one flow regulator, at least one control valve and / or at least one humidity regulator.

4. A controller according to any one of the preceding claims wherein the abnormal operating state includes or corresponds to a change in individual cycle time of the at least one pressurised fluid element.

5. A controller according to any one of the preceding claims wherein the abnormal operating state includes or corresponds to a change in total cycle time of the plurality of pressurised fluid elements.

6. A controller according to any one of the preceding claims wherein the abnormal operating state includes or corresponds to a change in pressure level of the at least one pressurised fluid element.

7. A controller according to Claim 6 wherein the change in pressure level is caused by a leak or an obstruction in the pressurised fluid system.

8. A controller according to any one of the preceding claims wherein the abnormal operating state includes or corresponds to a change in flow rate level of the at least one pressurised fluid element.

9. A controller according to any one of the preceding claims wherein the abnormal operating state includes or corresponds to a malfunction of the at least one pressurised fluid element.

10. A controller according to any one of the preceding claims wherein adjusting a pressurised fluid operation includes adjusting a pressure level of the at least one other pressurised fluid element.

11. A controller according to any one of the preceding claims wherein adjusting a pressurised fluid operation includes adjusting a flow rate level of the at least one other pressurised fluid element.

12. A controller according to any one of the preceding claims wherein adjusting a pressurised fluid operation includes adjusting an individual cycle time of the at least one pressurised fluid element.

13. A controller according to any one of the preceding claims wherein the controller is configured to, responsive to the abnormal operating state of at least one pressurised fluid element of the plurality of pressurised fluid elements: determine a plurality of possible configurations of the plurality of pressurised fluid elements; select a configuration from the plurality of possible configurations; and adjust the pressurised fluid operation of the at least one other pressurised fluid element in accordance with the selected configuration of the plurality of pressurised fluid elements.

14. A controller according to Claim 13 wherein the controller is configured to use an intelligent optimisation algorithm to select the configuration from the plurality of possible configurations.

15. A controller for controlling a pressurised fluid system comprising a plurality of pressurised fluid elements and a common pressurised fluid source, the common pressurised fluid source configured to supply pressurised fluid to the plurality of pressurised fluid elements, wherein the controller is configured to: determine a plurality of possible configurations of the plurality of pressurised fluid elements; use an intelligent optimisation algorithm to select a configuration from the plurality of possible configurations; andconfigure the plurality of pressurised fluid elements in accordance with the selected configuration of the plurality of pressurised fluid elements.

16. A controller according to any one of Claims 13 to 15 wherein the controller is configured to select the configuration from the plurality of possible configurations in accordance with an objective function corresponding to a model of the pressurised fluid system.

17. A controller according to any one of Claims 13 to 16 wherein the controller is configured to evaluate each of the plurality of possible configurations to determine at least one parameter of the plurality of pressurised fluid elements.

18. A controller according to Claim 17 wherein the at least one parameter of the plurality of pressurised fluid elements includes a total cycle time of the plurality of pressurised fluid elements and / or a pressurised fluid consumption of the plurality of pressurised fluid elements.

19. A controller according to Claim 17 or Claim 18 wherein the controller is configured to use a fitness function to evaluate each of the plurality of possible configurations to determine at least one parameter of the plurality of pressurised fluid elements.

20. A controller according to any one of Claims 13 to 19 wherein the controller is configured to select the configuration from the plurality of possible configurations based on at least one target parameter of at least one pressurised fluid element of the plurality of pressurised fluid elements and / or at least one target parameter of the plurality of pressurised fluid elements.

21. A controller according to Claim 20 wherein the at least one target parameter includes: a target individual cycle time of at least one pressurised fluid element of the plurality of pressurised fluid elements and / or a target total cycle time of the plurality of pressurised fluid elements; and / or a target pressurised fluid consumption of at least one pressurised fluid element of the plurality of pressurised fluid elements and / or a target pressurised fluid consumption of the plurality of pressurised fluid elements.

22. A controller according to Claim 14 or Claim 15 or any one of the preceding claims dependent thereon, wherein the intelligent optimisation algorithm is a swarm optimisation algorithm.

23. A controller according to any one of the preceding claims, wherein the pressurised fluid system is a pneumatic system, the plurality of pressurised fluid elements are pneumatic elements, and the common pressurised fluid source is a pneumatic source configured to supply compressed gas to the plurality of pneumatic elements.

24. A pressurised fluid system comprising a plurality of pressurised fluid elements, a common pressurised fluid source and a controller, the common pressurised fluid source configured to supply compressed fluid to the plurality of pressurised fluid elements, wherein the controller is in accordance with any one of the preceding claims.

25. A pressurised fluid system according to Claim 22 including at least one monitoring device, wherein the or each monitoring device is configured to monitor an operating state of the at least one pressurised fluid element, optionally wherein the or each monitoring device is configured to monitor a cycle time, a pressure level and / or a flow rate level of the at least one pressurised fluid element.

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