Method, apparatus and system for pump lifecycle assessment using trusted data

Abstract

The present disclosure relates to pump management. This disclosure proposes a method for querying lifecycle information of a pump. The method comprises collecting data of the pump; validating the collected data using trusted data, e.g., using a distributed ledger technology (DLT), and analyzing the collected data upon successful validation, to generate lifecycle information of the pump. In this way, a trusted and verifiable circularity insights / instructions are provided so that a trusted and verifiable lifecycle / circularity decision can be made. An apparatus for performing the method is proposed. The apparatus may be arranged on the pump side, or on a remote server side.

Description

[0001] METHOD, APPARATUS AND SYSTEM FOR PUMP LIFECYCLE ASSESSMENT USING TRUSTED DATA

[0002] TECHNICAL FIELD

[0003] This disclosure relates generally to the field of industrial equipment management, for example, management of a pump. For instance, this disclosure provides a method, an apparatus, and a system for performing pump lifecycle assessments.

[0004] BACKGROUND

[0005] Industrial pumps play a vital role in effectively transporting fluids across various processes and applications. The market’s growing demand for sustainable solutions is driving the need for pumps that can be dynamically improved and configured for optimal use, energy consumption and circularity.

[0006] SUMMARY

[0007] Businesses are increasingly aware of environmental factors, and are keen on adopting products that incorporate circularity to reduce their carbon footprint. However, two major issues hinder the effective incorporation of circularity: the lack of trusted data and the inability to efficiently analyze and make informed decisions on sustainability aspects such as repair, reuse, or recycling of pumps or pump parts.

[0008] The present disclosure thus has the objective to address these issues. In particular, an objective is to provide a method and system for lifecycle management of pumps, wherein trusted data is leveraged to generate actionable circularity insights and recommendations throughout the pump's lifecycle.

[0009] These and other objectives are achieved by the solutions of this disclosure as described in the independent claims. Advantageous implementations are further described in the dependent claims. A first aspect of this disclosure provides a method for performing lifecycle management of a pump. The method comprises the following steps: collecting data of the pump; validating the collected data using trusted data; and upon successful validation, analyzing the collected data, to generate lifecycle information of the pump.

[0010] For instance, validating the collected data using the trusted data may involve comparing the collected data (or a hash value thereof) with the trusted data (or a hash value of the trusted data). Trusted data is data that has been verified for accuracy, integrity, and authenticity by some validation mechanism, which ensures that it is free from tampering and corruption. Trusted data typically adheres to certain security standards and protocols, which provides confidence in its reliability for decisionmaking and operations.

[0011] Optionally, the pump data maybe collected periodically, or in an event-driven manner (e.g., in response to detecting an anomaly, such as water hammer), or in response to a request (e.g., a request for querying lifecycle information of the pump). The request maybe user triggered or automatically triggered.

[0012] In an implementation form of the first aspect, the collected data is validated using distributed ledger technology (DLT).

[0013] Validating the collected data by using DLT technology may comprise using at least one DLT network to validate the collected data. For instance, the validating may involve comparing the collected data (or a hash value of the collected data) with data stored in the at least one DLT network (or a hash value of that stored data). For example, the at least one DLT network may comprise multiple network nodes, wherein each network node may hold a copy of a ledger that includes trusted data (or includes hash values of said trusted data). The network nodes may perform a consensus mechanism to ensure that all the ledger copies (and thus all the data stored therein) match, thereby preventing unauthorized changes to the ledger data, making it trusted data. In an implementation form of the first aspect, the step of validating the collected data comprises: obtaining a first hash value corresponding to the data from a respective distributed ledger technology (DLT) network associated with the pump; calculating a second hash value of the collected data; and comparing the first hash value and the second hash value to validate the collected data.

[0014] This implementation form provides an example for validating the collected data using DLT. For instance, the DLT network maybe a blockchain network. The first hash value may be stored in a block of the blockchain network. The first hash value may be calculated by the pump (or by any other entity) at the time when the pump data was generated and is sent to the DLT network. For instance, at a time Ti, the data Di may have been generated at pump #1. The data Di may be stored locally in the pump Pi and / or may be sent to a server. In the meantime, a hash value Hi of the data Di may have been determined by the pump Pi or by the server, and may have been sent to the DLT network for storage. At a time T2, when lifecycle information is queried, the data Di may be collected from the pump Pi or from the server. Further, the hash value Hi may be obtained from the DLT network as the first hash value. A second hash value may be obtained by performing a hash operation on the data Di collected at the time T2. By comparing the first and the second hash value, it may be ensured that the data Di has not been tampered with, at least between time Ti and T2. In this way, the collected data can be trusted, i.e., is trusted data.

[0015] In a further implementation form of the first aspect, the first hash value is obtained from the DLT network based on a pump identification and / or timestamp of the data of the pump.

[0016] In a further implementation form of the first aspect, the data comprises one or more of: a pump status; wear and tear information; efficiency information; component degradation information.

[0017] In a further implementation form of the first aspect, the step of analyzing the collected data comprises: identifying a pattern, and / or a trend, and / or an anomaly related to the pump; generating one or more circularity insights for the pump; determining the lifecycle information based on the one or more circularity insights. In a further implementation form of the first aspect, the one or more circularity insights comprise one or more of: whether the pump is of a correct size for a current system requirement; a candidate operating parameter to be adjusted, so as to reduce an environmental impact, or increase a lifetime of the pump, or prepare the pump for reuse.

[0018] In a further implementation form of the first aspect, the lifecycle information comprise one or more of: a maintenance and / or refurbishment indication; an upgrade and retrofitting indication; a replacement schedule; and an estimated remaining life of the pump or a component thereof.

[0019] Based on the collected data, the one or more circularity insights and the lifecycle information can be generated at a component level of the pump, facilitating decisions regarding reuse, recycling, or disposal tailored to each component of the pump. For instance, analyzing the collected data can include determining an operational context of each component of the pump, such as temperature exposure, runtime, and type of pumped fluid. This enables informed decisions regarding circularity and lifecycle management on the component level.

[0020] Further, this disclosure can be used to shift the circularity assessment from assumption-based to fact-based reporting. Traditional circularity assessments may rely on average reused content across a product group. The present disclosure, however, can instead report on an actual reused component of a specific product (i.e., the pump), enabling customers to choose pumps based on verified sustainability performance and improved carbon footprint.

[0021] Optionally, the collected data may comprise material composition information of one or more components of the pump. The material composition information may describe specific materials and their elemental makeup used each of the one or more components. This information may help in determining recycling feasibility, regulatory compliance (e.g., per- and polyfluoroalkyl substances (PFAS) detection), and potential reuse. A second aspect of this disclosure provides an apparatus for performing lifecycle management of a pump. The apparatus is configured to: collect data of the pump; validate the collected data using trusted data; and upon successful validation, analyze the collected data, to generate lifecycle information of the pump.

[0022] In an implementation form of the second aspect, the apparatus is configured to validate the collected data using distributed ledger technology (DLT).

[0023] The trusted data may in this case be data of a DLT network.

[0024] In an implementation form of the second aspect, for validating the collected data, the apparatus is configured to: obtain a first hash value corresponding to the data from a respective DLT network associated with the pump; calculate a second hash value of the collected data; and compare the first hash value and the second hash value to validate the collected data.

[0025] In a further implementation form of the second aspect, the first hash value is obtained from the DLT network based on a pump identification and / or timestamp of the data of the pump.

[0026] In a further implementation form of the second aspect, the data comprises one or more of: a pump status; wear and tear information; efficiency information; component degradation information.

[0027] In a further implementation form of the second aspect, for analyzing of the collected data, the apparatus is configured to: identify a pattern, and / or a trend, and / or an anomaly related to the pump; generate one or more circularity insights for the pump; determine the lifecycle information based on the one or more circularity insights.

[0028] In a further implementation form of the second aspect, the one or more circularity insights comprise one or more of: whether the pump is of a correct size for a current system requirement; a candidate operating parameter to be adjusted, so as to reduce an environmental impact, or increase a lifetime of the pump, or prepare the pump for reuse.

[0029] In a further implementation form of the second aspect, the lifecycle information comprise one or more of: a maintenance and / or refurbishment indication; an upgrade and retrofitting indication; a replacement schedule; and an estimated remaining life of the pump or a component thereof.

[0030] In a further implementation form of the second aspect, the apparatus is arranged locally with respect to the pump.

[0031] Optionally, the apparatus maybe an internal part comprised in the pump. Alternatively, the apparatus maybe an external part attachable to the pump.

[0032] In a further implementation form of the second aspect, the apparatus is arranged remotely with respect to the pump.

[0033] Optionally, the apparatus may be an internal part comprised in a sever managing the pump. Alternatively, the apparatus maybe an external part attachable to the server.

[0034] The apparatus of the second aspect and its implementation can achieve the same advantages as the method of the first aspect and its respective implementations.

[0035] A third aspect of this disclosure provides a system comprising at least one apparatus according to the second aspect or any implementation form thereof.

[0036] A fourth aspect of this disclosure provides a computer program product comprising instructions which, when the program is executed by a computer, causes the computer to perform the method according to the first aspect or any implementation form thereof.

[0037] All steps that are performed by the various entities described in the present application as well as the functionalities described to be performed by the various entities are intended to mean that the respective entity is adapted to or configured to perform the respective steps and functionalities. Even if, in the following description of specific embodiments, a specific functionality or step to be performed by external entities is not reflected in the description of a specific detailed element of that entity that performs that specific step or functionality, it should be clear for a skilled person that these methods and functionalities can be implemented in respective software or hardware elements or any kind of combination thereof.

[0038] BRIEF DESCRIPTION OF DRAWINGS

[0039] The above-described aspects and optional implementations will be explained in the following description of specific embodiments in relation to the enclosed drawings, in which

[0040] FIG. 1 shows a flowchart of a method according to this disclosure;

[0041] FIG. 2 shows an exemplary flowchart of a method according to this disclosure;

[0042] FIG. 3 shows examples of an apparatus according to this disclosure;

[0043] FIG. 4 shows an exemplary flowchart of a method for generating in-use circularity insight;

[0044] FIG. 5 shows an exemplary flowchart of a method for generating end-of-use circularity insight;

[0045] FIG. 6 shows a first application scenarios of this disclosure;

[0046] FIG. 7 shows a second application scenarios of this disclosure; and

[0047] FIG. 8 shows a third application scenarios of this disclosure.

[0048] DETAILED DESCRIPTION OF EMBODIMENTS Illustrative embodiments of an analysis apparatus, a pump, a server, and a system are described with reference to the figures. Although this description provides a detailed example of possible embodiments and implementations, it should be noted that the details are intended to be exemplary and in no way limit the scope of the application.

[0049] In this disclosure, an embodiment / example may refer to other embodiments / examples. For example, any description including but not limited to terminology, element, process, explanation, and / or technical advantage mentioned in one embodiment / example is applicable to the other embodiments / examples. The same elements are labeled with the same reference signs and may function similarly or likewise.

[0050] FIG. i shows a flowchart of a method 100 according to this disclosure. The method is for performing lifecycle management of a pump and comprises the following steps:

[0051] Step 101: receiving a request for querying lifecycle information of the pump;

[0052] Step 102: collecting data of the pump;

[0053] Step 103: validating the collected data using trusted data, e.g. using DLT; and

[0054] Step 104: upon successful validation, analyzing the collected data, to generate lifecycle information of the pump.

[0055] It is noted that Step 101 is optional. An explicit request may not be necessary, e.g., the lifecycle query may be event-triggered. Alternatively, the lifecycle query may be triggered at pre-set intervals. It is further noted that when the validation fails, various measures can be taken, e.g., error reporting and / or data checking, etc. Nevertheless, this is not relevant to this disclosure, since no lifecycle information is to be generated in this case.

[0056] Optionally, the request may be automatically generated (e.g., may be event-triggered) or user-generated. The data may comprise one or more of a status, wear and tear information, efficiency information, component degradation information of the pump. It is noted that the examples of the data herein are just for illustration purposes. Any other pump data, which is relevant to the lifecycle of the pump, may be collected. Optionally, for validating the collected data, distributed ledger technology (DLT) such as blockchain may be used. For instance, during operation of the pump, when pump data (e.g., the status, the wear and tear information, the efficiency information, and / or the component degradation information) is generated and logged (e.g., locally in the pump and / or remotely in a server), a hash value of the corresponding pump data may be stored in a DLT network (e.g., a blockchain network). The pump is associated with the DLT network. When there are plurality of pumps, each pump is associated with a respective DLT network.

[0057] When lifecycle information is queried, and relevant data is collected, the collected data may be validated using trusted data, e.g. trusted because of the use of DLT, in this example using the hash value stored in the DLT network as a first hash value. For obtaining the first hash value from the DLT network, a pump identification and / or a timestamp of the collected data may be used to retrieve the first hash value from the DLT network (e.g., through a blockchain explorer). A second hash value of the collected data may be calculated based on the collected data. Then, the first hash value and the second hash value are compared. If they are the same, the validation is successful, meaning that the collected data is trusted and has not been tampered with.

[0058] Optionally, the collected data may comprise material composition information of one or more components of the pump. For knowing the exact material composition of a component may help, for example, to improve reuse rates. For instance, in the case of plastic components, accurate material data allows for precise blending of old plastics into new materials for a new component, without compromising product quality or safety of the new component. Moreover, this information can help in identifying whether a component contains regulated or hazardous substances, such as PFAS. The ability to verify whether a pump or a pump component includes such critical chemicals may be an important factor in both recycling decisions and regulatory compliance, especially when reusing components across different applications or industries.

[0059] FIG. 2 shows an exemplary flowchart of a method 200 according to this disclosure. The method 200 may be built based on the method shown in FIG. 1 and comprises the following steps. Step 201: A circularity or lifecycle question is given. This step may be built based on Step 101.

[0060] Step 202: Trusted pump lifecycle data is collected. This step may be built based on Steps 102 and 103.

[0061] Step 2041: Pump circularity and lifecycle analysis is performed.

[0062] Step 2042: Circularity insights or recommendations is generated.

[0063] Steps 2041 and 2042 may correspond to Step 104. Optionally, the step 104 of analyzing the collected data may comprise identifying a pattern, and / or a trend, and / or an anomaly related to the pump (as in step 2041); generating one or more circularity insights for the pump (as in step 2042).

[0064] Optionally, the one or more circularity insights comprise one or more of: whether the pump is of a correct size for a current system requirement; a candidate operating parameter to be adjusted, so as to reduce an environmental impact, or increase a lifetime of the pump, or prepare the pump for reuse.

[0065] For instance, one aspect in determining whether a pump or its component can be recycled or reused can be based on analyzing the collected pump lifecycle data. This may include collecting information of a material composition of a pump component. Furthermore, the expected lifetime of a pump component may depend on factors such as temperature exposure, runtime, and the nature of the pumped fluid. Collecting and analyzing the pump lifecycle data can help improving circularity / lifecycle management of the pump (and / or its components). For instance, it can prevent an example scenario where a pump component, which was exposed to highly toxic or corrosive substances, is later reused in an application requiring hygiene or high purity, such as a drinking water system.

[0066] Based on the one or more circularity insights, the lifecycle information can be determined. For instance, the lifecycle information comprises one or more of: a maintenance and / or refurbishment indication; an upgrade and retrofitting indication; a replacement schedule; and an estimated remaining life of the pump or a component thereof. In general, the lifecycle information may comprise a lifecycle decision (also referred to as a lifecycle action, or a lifecycle indication), which may comprise a during-life circularity decision / indication, an end-of-nth-life circularity decision / indication, or an end-of-life (grave) circularity decision / indication.

[0067] For instance, the during-life circularity decision may comprise one or more of the following:

[0068] Correct sizing (replacement, upgrades): Ensuring that the pump size matches the system requirements to avoid inefficiency;

[0069] Operational parameters: Adjusting and operating the pump, for instance, to achieve one or more of:

[0070] • reduce sustainability impact during life (e.g. energy management)

[0071] • increase life time

[0072] • facilitate for reuse (2nd life)

[0073] Remaining life: Estimating remaining life of the pump and its parts.

[0074] Pro-long life maintenance: Determining when and how to perform maintenance to increase performance, pro-long life and facilitate for reuse of pump and components.

[0075] Pro-long life refurbishment and spare parts: Evaluating when and how it is more sustainable (and cost-effective) to refurbish the pump and replace or upgrade components and spare parts.

[0076] • Reuse of replaced parts: Determining if the pump parts and components can be repurposed and reused in other applications (including by other customers).

[0077] • Recycling of replaced parts: Deciding on the best method to recycle the replaced components (and materials).

[0078] Pro-long life upgrades and retrofitting: Evaluating when it is more sustainable (and cost-effective) to upgrade or retrofit the pump instead of replacing the entire unit.

[0079] Replacement: Determining when it is optimal to replace the pump.

[0080] The end-of-nth-life circularity decision may comprise one or more of the following: Pump repurposing and reuse: Determining if the pump can directly be repurposed and reused in other applications (including by other customers).

[0081] Part / component reuse: Determining if the pump parts and components can be repurposed and reused in other applications (including by other customers).

[0082] Refurbishment and remanufacture: Assessing if and how refurbish or remanufacture of the pump or pump parts can create a second life cycle for the pump or its parts in other applications (including by other customers).

[0083] The end-of-life (grave) circularity decision may comprise one or more of the following:

[0084] Reuse vs. recycling: Deciding on the best method to either reuse or recycle the materials and components upon the end-of-life of the pump or a part.

[0085] Component / material recovery: Providing instructions for how to properly harvest and recover pump components and materials at pump end-of-life.

[0086] Responsible disposal: Ensuring any non-recyclable components are disposed of in accordance with environmental regulations.

[0087] When a component is reused, the associated data— such as material properties, prior usage, and operational data (e.g., recorded stresses)— can be transferred to the new pump. This ensures that circularity and lifecycle decisions for the reused component are made based on information at the component level. The reuse of components contributes significantly to lowering an overall CO 2 emissions of the new pump, making such reuse decisions not only economically beneficial but also environmentally advantageous.

[0088] According to the above, the solutions of this disclosure allow providing circularity insights and recommendations based on trusted data throughout a pump's lifecycle, thereby embodying the principles of the circular economy and offering a solution to better manage the environmental footprint and sustainability impact of the pump.

[0089] By enabling circularity and sustainability, the solutions of this disclosure can support the circular economy through detailed usage and maintenance records that facilitate the refurbishment and reintegration of pumps and pump components back into service, thus extending their operational life or giving them a second life. This can reduce the need for producing new pumps and lessens waste generated from discarded equipment.

[0090] By tracking the exact material composition and usage history of individual components, the disclosed solutions can provide a robust foundation for fact-based sustainability reporting. This eliminates reliance on generalized assumptions— such as average reused content across a product line— and enables reporting that reflects the actual reused material in each specific pump. As a result, stakeholders can make data-driven purchasing decisions and / or manage pumps aligned with their environmental targets, and may benefit from improved sustainability audits.

[0091] The solutions of this disclosure can also ensure regulatory compliance by automating recycling processes, which allows pump providers or operators to adhere to various regulations related to the reuse or recycling of industrial devices, such as those set by the United States Environmental Protection Agency for the recycling of hazardous wastes and precious metals found in pumps.

[0092] Furthermore, the solutions of this disclosure can enhance traceability and transparency in pump lifecycle management. Traditional methods of tracking a pump's history, maintenance records, and usage can be prone to errors, falsification, and lack of accessibility. This system uses a validation method, which is reliable and immutable, to verify the authenticity and integrity of pump data.

[0093] The solutions of this disclosure can assure quality and reliability for secondary markets, or in service scenarios like Pump-as-a-service where customers pay for water that is pumped by a pump. One pump could go from one customer to another. Customers often lack confidence in the condition and remaining service life of pre-owned pumps. With a detailed and validated history provided by this disclosure, they can avoid purchasing equipment that may soon fail or require unexpected repairs. It is noted that in this disclosure, any third party (e.g., customers or authorities) can also have access to the DLT network, e.g., the blockchain network that is associated with the pump, so that they can validate pump data by themselves. Further, the solutions of this disclosure allow introducing enhanced accountability. By maintaining a secure and tamper-proof record of events, it becomes possible to hold previous owners or operators accountable for their usage and maintenance behaviors, thus improving reliability and performance for subsequent owners.

[0094] FIG. 3 shows various examples of implementing an apparatus 30 according to this disclosure (dashed lines indicated possible implementations). Other implementations are possible. FIG. 3 specifically illustrates that in this example the apparatus 30 may be for a pump system comprising at least one pump 31 and a server 32. The pump 31 and sever 32 are configured to communicate with each other. The apparatus 30 may be a controller in this pump system. The apparatus 30 may control the pump system. The apparatus 30 does not have to comprise the pump 31, but can comprise the pump 31. The pump 31 can be a centrifugal pump. The pump 31 can be a pump for fluid or liquid, for instance, water.

[0095] The apparatus 30 is, in any case, configured to perform the method 100, 200 disclosed with respect to FIGs. 1-2. The apparatus 30 may be an analysis device or an analysis module, which may be associated with the pump system, or at least the pump 31.

[0096] Optionally, the apparatus 30 maybe arranged locally with respect to the pump 31. For instance, the apparatus 30 may be an internal component of the pump 31, or an external device attachable to the pump 31 (one of the implementations of the apparatus 30 indicated by dashed lines in FIG. 3).

[0097] Optionally, the apparatus 30 may be arranged remotely with respect to the pump 31. For instance, the apparatus 30 may be an internal component of the server 32, or an external device attachable to the server 32, or a device physically arranged separately from the server 32 and the pump 31 (another one of the implementations of the apparatus 30 indicated by dashed lines in FIG. 3).

[0098] The apparatus 30 could also be an extra device connected to both pump 31 and server 32 (another one of the implementations of the apparatus 30 indicated by dashed lines in FIG. 3). Notably, the apparatus 30 could be distributed and could be constituted by two or more of the example implementations shown in FIG. 3. In any case, the apparatus 30 may be any processing apparatus (e.g., a processor, a chip, or a chipset) capable of collecting pump data in response to the lifecycle query request (e.g., from the pump or the server), validating the collected data, and generating lifecycle information of the pump.

[0099] The pump 31, the server 32, and the corresponding apparatus 30 (e.g., integrated into 31 or 32) may have access to a validation entity 39. The validation entity 39 may be used to provide validation data for validating pump data stored in the pump and / or in the server. Any validation protocol that automatically validates data at the point of entry maybe utilized. For instance, the validation entity 39 maybe implemented using a DLT network, such as a (public) blockchain network. Alternatively, data with digital signatures and certificates maybe used by the validation entity 39.

[0100] For validating the collected data using trusted data, the apparatus 30 may be configured to obtain a first hash value from a DLT network. For instance, the first hash value may be stored in a block of a blockchain network. The obtained first hash value is then compared with a second hash value calculated based on the collected data.

[0101] For analyzing the collected data, the apparatus 30 may employ a machine learning algorithm, a rule-based algorithm, a statistical analysis algorithm, and the like, or a combination thereof.

[0102] In the following, an example of training the machine learning algorithm is described, which may be executed at the server 32 or in another location, such as a test center. Generally speaking, the training of the machine learning algorithm may comprise mounting a training pump unto a test rig which simulates the normal operation of a pump to collect training data, which may comprise sensed data, and maintenance action data (which includes insights, and recommended actions such as repair, refurbish, and recycle decisions). In other words, the maintenance action is the training label associated with the training dataset. The machine learning algorithm comprises a training logic (e.g., implemented as a neural network) to determine a set of features associated with the assigned label and the training dataset. The machine learning algorithm is then configured to learn, what set of features is indicative of the maintenance decision. Accordingly, the machine learning algorithm is configured to generate an inferred function which is capable of outputting the insights and the recommended actions during the in-use phase. The machine learning algorithm can be further trained by live data, i.e., data collected during real operation of a pump. This can train the machine learning algorithm to detect faults and / or events. The training of the machine learning algorithm can be supported with computer-simulated data as well.

[0103] FIG. 4 shows an exemplary flowchart of a method 400 for generating in-use circularity insight. The method may be applied to the apparatus 30 of this disclosure described with respect to FIG. 3 and may be built based on the methods 100, 200 shown in FIGs. 1-2. The apparatus 30 maybe referred to as an analytics engine (AE) in this disclosure (e.g., in FIGs. 4-8).

[0104] Notably, the terms “in-use circularity insight” refers to insights that the analytics engine is adapted to generate while the pump 31 or its component is actively being used for the intended purpose. In other words, it may relate to a pump 31 or component that has been newly manufactured, or refurbished but in-use.

[0105] The analytics engine 30 has access to a validation entity and is able to collect trusted pump or component usage pattern / operational data (such as, but not limited to, electricity usage, run time, flow rate, pressure, etc.) to provide circularity insights to improve the circularity of the pump / component (described in more details below).

[0106] In some non-limiting examples, based on the insights, the analytics engine 30 maybe further configured to provide associated recommended actions. It should be understood that the recommended actions relates to actions to improve the circularity of the pump or component (described in more details below).

[0107] Optionally, both the insight generated by the analytics engine 30, together with the optional recommendations maybe then added to the validation entity as part of history data of the pump 31. The method 400 provides an overview of the in-use circularity insight and recommendation process and comprises the following steps.

[0108] Step 401: The analytics engine (apparatus 30 of this disclosure) is configured to receive pump data. How the analytics engine 30 receives the pump data is not limited. In some embodiments, the pump data is received from the pump 31 or the server 32 in response to a controller actuating the analytics engine to answer a question (e.g., “determine the remaining life of pump #1”) at the server 32. In another embodiment, the pump data may be received periodically, or in an event-driven manner (e.g., detecting a water hammer at pump #1) by the analytics engine 30. In another embodiment, the pump data maybe received each time the pump data changes (and e.g. only the changes of the pump data may be received), or may be received at pre-set intervals like once a minute or every five minutes. In general, the analytics engine 30 maybe configured to perform real-time circularity insight & recommendation analysis. After the data is collected, the analytics engine 30 is configured to validate the collected data using trusted data based on FIGs. 1-3 mentioned above, so that the collected data is trusted.

[0109] Step 402: The analytics engine 30 is configured to process the collected data to identify patterns, trends and anomalies that provide one or more circularity insights. For instance, by processing the data, the analytics engine assesses the current pump status against sustainability criteria, considering factors like efficiency, wear and tear and part degradation. Based on the analysis the analytics engine provides insights about:

[0110] • Correct sizing: e.g., identifying if the pump 31 is over or under sized for the current system requirements; and / or

[0111] • Operational parameters: e.g., identifying potential changes in operating parameter to minimize environmental impact, increase lifetime or prepare the pump 31 for potential reuse. Step 403: After determined the one or more circularity insights, the analytics engine 30 is configured to determine the associated recommended actions. Based on the insights, the recommended actions may comprise:

[0112] • Maintenance and refurbishment recommendations: e.g., recommendations for proactive maintenance or refurbishment (including spare part management) that can prolong the useful life and efficiency of the pump 31, reduce waste, and optimize resource utilization; and / or

[0113] • Upgrades and retrofitting options: e.g., suggestions for when it is more sustainable and cost efficient to upgrade or retrofit the pump 31 to extend its lifespan or improve performance over replacement; and / or

[0114] • Replacement schedule: e.g., recommendations for the optimal timing for repair / replacement based on predictive insights and sustainability considerations.

[0115] • Estimating the remaining life of the pump 31 and its parts.

[0116] Step 404: A controller (e.g., at the server 32 or a control center or the apparatus 30) gathers the generated insights and recommendations, e.g., through visualization tools and dashboards. The controller is adapted to review and approve the recommended action.

[0117] Notably, any of the insights and / or the recommendations may be comprised in the lifecycle information generated in step 104 of FIG. 1, or as in step 2032 of FIG. 2.

[0118] For each of the steps 401-404, the validation entity 39 may be updated for the associated pump / component. For instance, the recommendation and executed actions are documented and logged in the pump 31 and / or in the server 32, and a hash value of the documented recommendation and executed actions are stored in the validation entity, e.g., a block in a blockchain network associated with the pump. In this way, the recommendation and executed actions are documented as trusted data to maintain a detailed and immutable record of the operations. FIG. 5 shows an exemplary flowchart of a method 500 for generating end-of-use circularity insight. The method 500 may be applied to the apparatus 30 of this disclosure described with respect to FIG. 3 and maybe built based on the methods 100, 200 shown in FIGs. 1-2.

[0119] Notably, the term “end-of-use circularity insight” refers to insights and recommendations that the analytics engine may generate while a pump 31 or its component is no longer actively being used for the intended purpose. In other words, it may relate to a pump 31 or component that has been removed from active use following the end of a project, or following a replacement.

[0120] For instance, the end-of-use circularity insight may comprise one of whether (i) the pump or part is at its end of life (and therefore needs to be recycled), or (ii) the pump 31 or part can still be used. In some non-limiting embodiments, based on the insights, the analytics engine may be further configured to provide associated recommended actions, so as to improve the circularity of the pump 31 or component.

[0121] The method 500 comprise the following steps.

[0122] Step 501: The analytics engine (apparatus 30 of this disclosure) is configured to collect pump data. As an example, the analytics engine 30 may receive the pump data when a pump or a part thereof is near the end of its intended use (e.g., several days or weeks before decommissioning). This step may share the same features as step 401. The analytics engine 30 is also configured to validate the collected data as mentioned in FIGs. 1-3.

[0123] Step 502: The analytics engine 30 may be trained (or may employ a trained neural network model) to analyze the collected data and is configured to output one or more circularity insights. In some non-limiting embodiments, the one or more circularity insights may include:

[0124] • Determining, based on the data, if the pump 31 or part is suitable for direct repurposing;

[0125] • If not, determining, based on the data, if the pump 31 is suitable for refurbishment; If not, determining, that the pump 31 is at its end of life, and should be recycled.

[0126] How the determination is made is not limited. It is contemplated that the determination may be done by analyzing the estimated remaining life of the pump 31 or its part. In other words, if the estimated remaining life is above a first threshold, the analytics engine may determine that the pump is suitable for direct repurposing. On the other hand, if the remaining life is below a second threshold, the pump is determined that it is at its end of life and should be recycled. Needless to say, other rules or methods may be used to make the determination.

[0127] Step 503: Having determined the one or more circularity insights, the analytics engine 30 is configured to determine the associated recommended actions. In some non-limiting embodiments, the one or more recommended actions are:

[0128] • Pump repurposing and reuse (as in step 504): determining that the pump can directly be repurposed, updating the inventory, and identifying a pending account to which said pump 31 can be used;

[0129] • Part / component reuse (as in step 504): determining that the part can be directly repurposed, updating the inventory, and identifying a pending account to which said part / component can be used;

[0130] • Refurbishment (as in step 505): When the repurposing / reuse is not a viable option, determining, based on the environmental factors and economic factors, if and how to refurbish the pump to create a second (or further) life cycle for the pump 31, e.g.:

[0131] ■ Recycling: determining the best method (economically and sustainably) to recycle the materials and components by ranking options;

[0132] ■ Component / material recovery: providing instructions for how to properly harvest and recover pump components and materials in accordance with industry practice and regulations. Responsible disposal (as in step 506): providing instructions to ensure any non-recyclable components are disposed of in accordance with industry practice and regulations.

[0133] Step 504: The analytics engine 30 determines that the pump or its part is fit for operation without any or minimal repair. As such, the analytics engine maybe configured to provide the reuse action / instruction to a controller, such that the controller can update the inventory information and determine a client for which said pump / part can be used.

[0134] Step 505: The analytics engine 30 determines that the pump requires refurbishment. As such, the analytics engine is configured to provide the refurbishment action / instruction to the controller, such that the controller can identify the part that requires repair, and in some instances maybe even configured to place an order for said part.

[0135] Step 506: The analytics engine 30 determines that the pump / part is no longer useful. As such, the analytics engine is configured to provide the recycle action / instruction to the controller. For example, the analytics engine may be configured to identify where the rare metal of the pump is, and the method of extraction. Additionally, the analytics engine may include regulation logic and provide instructions on the recycling abiding with local recycling regulation.

[0136] Notably, any of the reuse action / indication, the refurbishment action / indication, and the recycle action / indication maybe comprised in the lifecycle information generated in step 104 of FIG. 1, or as in step 2032 of FIG. 2.

[0137] For each of the steps 501-504, the validation entity 39 may be updated for the associated pump / component. For instance, the recommendation and executed actions are documented and logged in the pump 31 and / or in the server 32, and a hash value of the documented recommendation and executed actions are stored in the validation entity, e.g., a block in a blockchain network associated with the pump. In this way, the recommendation and executed actions are documented as trusted data to maintain a detailed and immutable record of the operations.

[0138] FIGs. 6-8 shows various application scenarios of this disclosure.

[0139] For instance, FIG. 6 shows an exemplary flowchart in a case scenario where a pump user prompts a request like “Are we at the end of the lifetime” or “Is there enough lifetime left”. FIG. 7 shows an exemplary flowchart in a case scenario where the pump user prompts a request like “What should I do with the pump that I am replacing”. FIG. 8 shows an exemplary flowchart in a case scenario where the pump user prompts a request like “How should this discarded pump be recycled”.

[0140] The present disclosure has been described in conjunction with various embodiments as examples as well as implementations. However, other variations can be understood and effected by those persons skilled in the art and practicing the claimed matter, from the studies of the drawings, this disclosure, and the independent claims. In the claims as well as in the description the word “comprising” does not exclude other elements or steps and the indefinite article “a” or “an” does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in the mutually different dependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation.

Claims

Claims1. A method (100) for performing lifecycle management of a pump (31), the method (100) comprising: collecting (102) data of the pump (31); validating (103) the collected data using trusted data; and upon successful validation, analyzing (104) the collected data, to generate lifecycle information of the pump (31).

2. The method (100) according to claim 1, wherein the collected data is validated using distributed ledger technology, DLT.

3. The method (100) according to claim 1 or 2, wherein the step of validating (103) the collected data comprises: obtaining a first hash value corresponding to the data from a respective DLT network (39) associated with the pump (31); calculating a second hash value of the collected data; and comparing the first hash value and the second hash value to validate the collected data.

4. The method (100) according to claim 3, wherein the first hash value is obtained from the DLT network (39) based on a pump identification and / or a timestamp of the data of the pump (31).

5. The method (100) according to any one of claims 1 to 4, wherein the data of the pump (31) comprises one or more of: a pump status; wear and tear information; efficiency information; component degradation information.

6. The method (100) according to any one of claims 1 to 5, wherein the step of analyzing (104) the collected data comprises:identifying a pattern, and / or a trend, and / or an anomaly related to the pump(3i); generating one or more circularity insights for the pump (31); and determining the lifecycle information based on the one or more circularity insights.

7. The method (100) according to claim 6, wherein the one or more circularity insights comprise one or more of: whether the pump (31) is of a correct size for a current system requirement; a candidate operating parameter to be adjusted, so as to reduce an environmental impact, or increase a lifetime of the pump (31), or prepare the pump (31) for reuse.

8. The method (100) according to any one of claims 1 to 7, wherein the lifecycle information comprises one or more of: a maintenance and / or refurbishment indication; an upgrade and retrofitting indication; a replacement schedule; an estimated remaining life of the pump or a component thereof.

9. An apparatus (30) for performing lifecycle management of a pump (31), the apparatus being configured to: collect data of the pump (31); validate the collected data using trusted data; and upon successful validation, analyze the collected data, to generate lifecycle information of the pump (31).

10. The apparatus (30) according to claim 9, wherein the apparatus (30) is configured to validate the collected data using distributed ledger technology, DLT.

11. The apparatus (30) according to claim 9 or 10, wherein for validating the collected data, the apparatus is configured to:obtain a first hash value corresponding to the data from a respective distributed ledger technology, DLT, network (39) associated with the pump (31); calculate a second hash value of the collected data; and compare the first hash value and the second hash value to validate the collected data.

12. The apparatus (30) according to any one of claims 9 to 11, wherein the first hash value is obtained from the DLT network (39) based on a pump identification and / or timestamp of the data of the pump (31).

13. The apparatus (30) according to any one of claims 9 to 12, wherein for analyzing of the collected data, the apparatus being configured to: identify a pattern, and / or a trend, and / or an anomaly related to the pump (31); generate one or more circularity insights for the pump (31); determine the lifecycle information based on the one or more circularity insights.

14. The apparatus (30) according to any one of claims 9 to 13, wherein the apparatus is arranged locally with respect to the pump (31).

15. The apparatus (30) according to any one of claims 9 to 13, wherein the apparatus is arranged remotely with respect to the pump (31).

16. A system (30, 31, 32, 39) comprising at least one apparatus (30) according to any one of claims 9 to 15.

17. A computer program product comprising instructions which, when the program is executed by a computer, causes the computer to perform the method (100) according to any one of claims 1 to 8.

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