| D1.1 | Report on Use-case Definitions and Evaluation Metrics | The main objective of the task underpinning this deliverable is to perform a high-level assessment and description of the use cases and show how they fulfil the main objectives of the SMART-PDM project and how they will advance the state of the art in its field.
|
| D1.2 | Requirements for Use-cases’ Information Architecture | The main objective of the task underpinning this deliverable is to complete the high-level assessment and description of the use cases and determine the desirable information architecture to use at each case. For this purpose, this document aims at describing the functionalities that the monitoring system should comply with in the form of requirements; while presenting the different options for monitoring and signal management solutions to be used in the different use-cases that compose the SMART-PDM project. Proposed solutions are based on the needs of every use case and the discussion section present in this deliverable aims at detecting similarities, cooperation, and interoperation possibilities among all use-cases. |
| D1.3 | Privacy, Security and GDPR | This document describes the definition of privacy, security and General Data Protection Regulation (GDPR) related design and development considerations that will be carried out in SMART-PDM project. Following Deliverable 1.2, this deliverable focuses its work on how privacy and security will have to be handled on platform basis. This work also focuses on the implications that the new GDPR European legislation on the systems being developed, particularly on the handling of data Integration. It will initially be defined as per ten use cases for rolling mill, vehicles, wind turbines, seed drill/woodchipper, hydro power plants, milling machines, manufacturing laboratory, and home appliances. The home appliances use-case has specific considerations on privacy and ways to handle end-user specific data. All use cases have common cybersecurity considerations. In addition to GDPR, country specific ethics and privacy laws are also considered and elaborated in a separate section of this document. |
| D2.2 | Existing and desirable sensors to develop predictive maintenance for each use case | Monitoring of signals requires the identification of suitable sensors solutions. Sensor selection, deployment and characterization is therefore a fundamental step for the implementation of condition-based maintenance strategies and technical developments envisaged within the project.
Initially in chapter 2 this document details the signals that will be used to determine the condition of the production equipment components and the sensors systems currently available on the demonstrators.
In chapter 3, this document proposes a set of guidelines for selecting the sensor system solutions best suited to monitor the condition of use cases equipment. Such guidelines are intended to be applicable to different technological fields and will be tested in the context of the end users’ demonstrators. |
| D2.1 | Integration and Interconnectivity Analysis | This document describes the definition of integration activities to be carried out in the SMART-PDM project. It includes brief descriptions of each integration definition, functional description, integration point description, requirements and constraints, activities and roles, risk analysis and schedule of integration activities. Integrations will initially be defined according to ten use cases for rolling mill, vehicles, wind turbines, seed drill / wood chipper, hydro power plants, milling machines, production laboratory and household appliances. The methods, tools and software used in the project are explained in the document. With the predictive maintenance method in the project, it is aimed to prevent a possible error that may occur with regular maintenance to the stands. In addition, saving time and costs is one of the main objectives of the project. |
| D2.3a | Network and Storage Architecture Report | This report presents the SMART-PDM network and storage architecture. The architecture facilitates data to be acquired from multiple sources, analytics to be provided by multiple vendors, and results to be communicated to all relevant parties. The architecture is based on standards and open source components.
Arrowhead framework provides tools for connecting data sources and data consumers by authenticating and authorising devices for communication between each other. This is done by Arrowhead managing a set of programmable rules on which device can access what devices within an Arrowhead cloud. Multiple Arrowhead clouds can also communicate with each other facilitating communication between devices managed by different Arrowhead servers. Small applications for setting up Arrowhead servers and utilizing Arrowhead are built as open source projects on GitHub under the VTT Operation and Maintenance organization (VTT Operation and Maintenance, 2021).
MIMOSA relational database model called CRIS (Common Relational Information Schema) is used in defining data structures that support maintenance operations. The implemented application requires only a subset of data structure definitions from MIMOSA. The data structure is used to store data such as measurement data and diagnostics results. The MIMOSA database model is run on open source MariaDB database server.
REST APIs are built for bi-directional data transfer between the devices and the MIMOSA databases. REST APIs provide an easy to use and secure interface to the database utilizing HTTPS protocol. An open source project Django REST framework is used to build the REST APIs. |
| D2.3b | Sensors, network and storage architecture configuration and software | The purpose of the report is to describe all the software at a general level needed to obtain data from sensor elements, data transfer to the gateway, applied in edge computing and for data transfer from the gateway to the selected cloud. Each use case has their own requirements for the sensors and for the data acquisition and data storage systems. The report presents the software modules related to each use case in their own sections. The PDM software implementations are covered in WP4 and are not included in this document. |
| D2.4 | Final application Report | The purpose of the report is to summarize all the activities done in WP2 during M4-M27 of SMART-PDM. Each of the use case have their own requirements for the sensors and for the data acquisition and data storage systems. However, common architecture using Arrowhead Framework and Mimosa data model has been created to fulfil the needs of the whole consortium. The report presents the summary related to each of the use cases in their own sections. |
| D3.1 | Physical Signal and Attribute Analysis
related to each Use Case | SMART-PDM Task 3.1 deals with the third step of CRISP-DM, namely signal segmentation, outlier removal, feature extraction, clustering, feature comparison and knowledge extraction. This way, it will be possible to identify whether the captured signals are enough to identify the deterioration, to estimate the remaining useful life of subsystem and systems. During the process, it may also be necessary to define new variables by combining different input signals using different mathematical models, which can better serve the purposes of model predictions that take place in later tasks.
The deliverable will attempt to give insight into the types of signals and data attributes that will be pre-processed under this task for the purpose of modelling needed as part of Tasks 3.2 and 4.2. |
| D3.2a | Implemented Advanced Data Analytics for Diagnostics, Prognostics and Advisory generation | Manufacturing accounts for 15.96% of EU GDP (World Bank), is undergoing immense yet gradual Industry 4.0 transformation with the help of advancements including predictive maintenance, whose market size is estimated to be worth USD 4.9b by 2021 (Markets and Markets).
SMART-PDM’s objective is to provide diagnosis and prognosis information while rendering the underlying technology financially feasible. The project improves Overall Equipment Effectiveness (OEE) of the manufacturing industry by, e.g., implementing cost-efficient Predictive Maintenance (PDM) utilizing low-cost sensors.
One of the key techniques in PDM is data science, i.e., analysis and modelling of measurement data. When combined with domain knowledge, e.g., understanding of possible failure mechanisms, automatic and reliable diagnostics, prognostics, and decision support can be provided. After describing the data analysis techniques used in the different use cases of the project in Deliverable 3.1, this document aims at describing the generalities of advanced data analytics related to diagnosis, prognosis, and advisory generation, to latter describe the different actions taken in the different use cases of the SMART-PDM project. |
| D3.2b | Data Analytics Software | Task 3.2 is primarily regarding the identification of the techniques required to:
Diagnose the current system’s status: this can be based on prior knowledge of the system, by experts.
Forecast the evolution of the system in order to determine future maintenance points.
Prescribe new working conditions for the best performance, at the same time, attending asset management policies.
While D3.2a focuses on the results obtained from the implementation of such techniques, D3.2b, being a software deliverable, focusses on the software that is used to obtain such results. Similar to D3.2a being distinguished from D4.2a in that industrial results are only included in D4.2a and not D3.2a, D3.2b is intended to emphasise parts of the software relating to the overall predictive and prescriptive techniques whereas D4.2b places effort to include software features relevant to industrial applications. |
| D3.3a | Report containing the Business Analytics | SMART-PDM’s objective is to acquire manufacturing data to provide diagnosis and prognosis information while rendering the underlying technology financially feasible.
This task will deal with the business level considerations needed to implement new models and services with tasks such as:
· Identify added value in the predictive maintenance service that can be created.
· Assess related costs for different deterioration stages comparing them with the costs of a breakdown.
· Determine the impact of predictive maintenance on the maintenance team and spares allocation.
· Evaluate if proposed sensor system is enough or excessive for the designed predictive maintenance service.
Therefore, in this task the consortium members will try and figure out if it was financially worth implementing certain actions.
In order to have an apples-to-apples comparison, one way may be to translate all known PdM related marginal costs and benefits to present time in a given currency. And if the costs outweigh benefits, the reasoning is such that there may not be a sense in implementing or investing in such a PdM set up after all. Alternatively, since migration from reactive and preventative maintenance types to predictive type maintenance may involve several steps, this cost versus benefit calculation may give an indication of whether it is worthwhile taking any of the steps during the implementation.
The following section presents various solution providers’ approaches to calculating such financial metrics. |
| D3.3b | Business Analytics Software | SMART-PDM’s objective is to acquire manufacturing data to provide diagnosis and prognosis information while rendering the underlying technology financially feasible.
This task will deal with the business level considerations needed to implement new models and services with tasks such as:
· Identify added value in the predictive maintenance service that can be created.
· Assess related costs for different deterioration stages comparing them with the costs of a breakdown.
· Determine the impact of predictive maintenance on the maintenance team and spares allocation.
· Evaluate if proposed sensor system is enough or excessive for the designed predictive maintenance service.
Therefore, in this task the consortium members will try and figure out if it was financially worth implementing certain actions.
In order to have an apples-to-apples comparison, one way may be to translate all known PdM related marginal costs and benefits to present time in a given currency. And if the costs outweigh benefits, the reasoning is such that there may not be a sense in implementing or investing in such a PdM set up after all. Alternatively, since migration from reactive and preventative maintenance types to predictive type maintenance may involve several steps, this cost versus benefit calculation may give an indication of whether it is worthwhile taking any of the steps during the implementation.
The following section presents various solution providers’ approaches to calculating such financial metrics. |
| D4.1 | End users’ requirements related to FMECA analysis | In this report Failure Mode Effects and Criticalities Analysis (FMECA) and Criticality Matrix (CM) for each of the use cases of the project are included. The aim of carrying out FMECA and CM analysis has been to identify the failure modes, their occurrence rates, severities and effects that will constitute the base for the development of an effective maintenance management. This will be crucial for the development of the predictive maintenance policies / rules that will be derived as the result of this project.
In most of the uses cases, there already exists knowledge about the systems, machines, or processes that will be analysed as use-cases. This is a valuable source of information for predictive maintenance tools development, since it allows pointing out the most relevant parts, issues and possible solutions (from the maintenance point of view). This knowledge has been crucial to apply FMECA and CM methodologies.
For some use-case owners, this has been the first time they have implemented FMECA and CM methodologies. All partners have found these tools to be effective to extract relevant information about the functioning of their systems that serve to identify most relevant failures, set priorities and response actions. In fact, these tools have served, to structure the available knowledge about the systems of each use case. |
| D4.2a | Report containing the implementation of predictive maintenance techniques – first prototype | The aim of the report is to describe the methods of predictive maintenance techniques that have been implemented in various pilots. The report presents each use case as a whole, including methods for operation and predictive maintenance, which will enable the machine to be operated better and, on the other hand, provide an estimation of the Remaining Useful Life (RUL) of the selected components. In other words, this report describes the first prototype on a case-by-case basis to which preventive maintenance has been applied. |
| D4.2b | Software for the Implementation of Predictive Maintenance Techniques – First Prototype | This document describes the system requirements for and the features of the use-case D4.2b as well as the description of the graphical user interface and the results of the first prototype. |
| D4.3a | Report describing the implementation of predictive maintenance techniques – final prototype | The aim of the deliverable is to describe the methods of predictive maintenance techniques that have been implemented in various pilots. The report presents each use case as a whole, including methods for operation and predictive maintenance, which will enable the machine to be operated better and, on the other hand, provide an estimation of the Remaining Useful Life (RUL) of the selected components. In other words, this report describes the final prototype on a case-by-case basis to which preventive maintenance has been applied.
This deliverable builds upon Deliverable 4.2a which was prepared and submitted about 1 year ago. Since then, certain partners had significant progress, and understandably certain partners did not, since according to their work plan their work may have finished already. Therefore, this deliverable will only present the significant progress over D4.2a, if any, and it will not repeat content from D4.2a. |
| D4.3b | Software for the Implementation of Predictive Maintenance Techniques – Final Prototype | This deliverable explains the details of the software that is used to generate the results presented in Deliverable 4.3a. Information such as the graphical user interface (GUI), system requirements, configuration data, parameters,
One thing to note is that since there was plenty of software described in Deliverable 4.2b in 2021, this deliverable, namely Deliverable 4.3b, only reports on the significant updates over Deliverable 4.2b. |
| D4.4 | Compliance with regulations and standards | The adoption of predictive maintenance solutions depends on a legal framework of approval built on regulation, partly driven by policy, and an array of certification processes and standards driven by industry. As predictive maintenance solutions are deployed successfully in new market areas, regulation and certification can lag behind thereby creating barriers to adoption.
Similarly, a lack of standards and associated certification and validation methods can hold back deployment and the creation of supply chains and therefore, slow market uptake. In some areas of predictive maintenance, the market will move ahead and wait for regulation to react, but in many application areas existing regulation can present a barrier to adoption and deployment. Most notably in applications where there is close interaction with people, either digitally or physically, or where predictive maintenance solutions are operating in safety or privacy critical environments.
PRCS issues are likely to become a primary area of activity for the SMART-PDM project partners. Increasingly it is regulation that is the primary lever for the adoption of predictive maintenance solutions. Similarly, the development of standards, particularly around data exchange and interoperability will be key to the creation of a European AI market place. Establishing how to certify AI will underpin the development of trust that is essential for acceptance and therefore Adoption.
As the field of PDM matures, it needs to be aware of the regulations, policies, and standards that will both impose boundaries as well as provide guidance for operations. Policies, Regulations, and Standards provide information on how to design or operate something, but with different degrees of applicability.
Policies include, public policies (such as environmental policy which provides guidance for the pollution prevention) as well as organizational policies (such as maintenance policies)
Regulations and certifications (such as railways, aeronautics, or wind energy) typically impose binding rules of engagement and are imposed by regulatory bodies that are responsible for a particular field.
Standards, in contrast, are community-consensus guidelines that are meant to provide benefit to the community by describing best practices. Adoption of such guidelines is entirely voluntary but may provide benefits by not having to reinvent the wheel and for finding common ground amongst other adopters.
At last, it is important to note that they also have different focus: while regulations are generally concerned with safety, policies are often concerned with operational savings, while standards provide pe-competitive information about best practices which, when adopted, may allow to benefit from common rules of engagement.
The purpose of the report is to provide an overview of the current regulations, policies, and standards in the field of Predictive maintenance and concretely for SMART-PDM solutions. |
| D5.1 | Dissemination Plan | The dissemination activities of the SMART-PDM project are part of Task 5.3 (Dissemination). The primary objective for dissemination is to create strong awareness of the SMART-PDM project and technologies both on the European but also on the national level, with the aim to multiply its impact and subsequent exploitation chances. The planned standardisation efforts will foster a further industrialisation of the SMART-PDM results and create a solid base for investment and commitment of the SMART-PDM partners. Commercialisation will be supported by giving SMART-PDM results – from today’s perspective the developed techniques around data analytics. The proper set of dissemination activities will be carefully studied, starting with the identification of all the different stakeholders that may be interested in the main outcomes of the SMART-PDM project and to raise their individual interest. |
| D5.2 | Preliminary Exploitation Plan | SMART-PDM’s objective is to acquire manufacturing data to provide diagnosis and prognosis information while rendering the underlying technology financially feasible.
In order to achieve this goal, we created a very complementary and conducive consortium with 11 use case demonstrators ambitious to see the trial results, 9 integration & tools providers and 9 technology and knowledge providers all waiting to test out and further improve their technologies, all with a balanced breakdown of 10 SMEs, 10 Large Enterprises and 6 RTOs which are spread across 5 countries. |
| D5.3 | Exploitation Plan | This work package aims at the dissemination and exploitation activities carried out within the project by its partners. There are 3 tasks under this work package:
• T5.1 Demonstrators (M24-M36)
• T5.2 Exploitation (M18-M36)
• T5.3 Dissemination (M6-M36)
In order to effectively develop the operating strategy of the SMART-PDM project, the operating strategy had to be built efficiently, and the individual strategies were considered in parallel. In order to provide transparency so that the partners understand exactly what is being done by the partners, the exploitation and planning strategy was carried out together, in various workshops organized by the exploitation manager. As can be seen in Figure 1, the communication, dissemination and exploitation activities of Industry 4.E The Digital Industry Excellence Initiative [1] are defined.
In order to exploit and disseminate in the SMART-PDM project, the main objective proposed was to use the project results in the best possible way, and to create awareness regarding the research and development pipelines of the project: from scientific results to technological advances in success stories in cases of industrial use.
As stated in the exploitation of the T5.2 SMART-PDM project, the main goal of the exploitation activity is a European-level product aimed at doubling its effectiveness and the potential for subsequent exploitation. This goal can be divided into four sub-goals:
• First, evaluate the optimal business model and define the corresponding utilization plan.
• Then contact users who may be interested in purchasing the product or service.
• Third, share the SMART PDM platform with the end-user community.
• And fourth, build and inspire an online knowledge community that connects individuals with organizations interested in this subject.
The expected result of this activity is a usage plan to reach the desired market based on the business model selected (tool business, service business, product business, etc.). |
| D5.5 | Dissemination Results | The dissemination activities of the SMART-PDM project are part of Task 5.3 (Dissemination). The primary objective for dissemination is to create strong awareness of the SMART-PDM project and technologies both on the European but also on the national level, with the aim to multiply its impact and subsequent exploitation chances. Commercialisation will be supported by giving SMART-PDM results. The proper set of dissemination activities were carefully defined and planned in Deliverable 5.1 [1], starting with the identification of all the different stakeholders that may be interested in the main outcomes of the SMART-PDM project and to raise their individual interest. |