The second (sixth overall) full consortium meeting of 2017 was held between 8th and 10th of May. This time it was hosted by VTT at their new Center for Nuclear Safety located in Espoo, Finland. The three day event gathered 65 participants from all of the participating countries. The program was more technologically oriented and contained a long open space session, where partners could present their work within the project. The tight program allowed some time to enjoy the wonderful Finnish spring weather.
Finnish use-case was prominently on display at the Open Space session at the MANTIS consortium meeting. The floor in the first open space room was dedicated to the Finnish use case. Presented were Nome, Wapice, Fortum, VTT and Lapland University of Applied Sciences (UAS). Each partner presented their work done in the Finnish use case. Wapice and Fortum presented their HMIs (IoT Ticket and TOPi respectively). Nome and VTT presented their measurement systems (NMAS and the affordable sensor research respectively) and finally Lapland UAS provided the database and REST interface that allows each partner to share and access data beyond organizational boundaries. The second room had most use cases represented. Of note was XLABs common MANTIS user interface demo that can be connected to the Finnish use case platform.
The Finnish use case is centered on a flue gas recirculation blower located in Fortum’s Järvenpää power plant. The blower is classified as a critical component in the energy production process and is monitored closely. In this use case Wapice, Nome and VTT have all provided their own sensors or virtual sensors to monitor the performance and condition of the blower. In addition, Lapland UAS has a few Wzzard sensors, made by B+B Electronics/Advantech, provide some additional measurement data bulk. However these are not related to the Järvenpää case. This measurement data is stored, using the REST interface developed by Lapland UAS, in the MANTIS database that is based on the MIMOSA data model.
The REST interface and MIMOSA database mapper provides a simple an interface, which is both easy to use and to integrate, between different applications and systems. It provides basic CRUD –functionalities and contains a mapper that maps measurement system specific data formats and structures into MIMOSA compliant data structures to ensure interoperability and compatibility with the MIMOSA data model. It is widely in use in the Finnish use case and research partners from both Slovenia and Hungary have shown interest towards utilizing MIMOSA in their use case.
Goizper and IK4-TEKNIKER will be present at the Hannover Messe from 24 to 28 April 2017 presenting Smart G, a data acquisition module for clutch-brake monitoring.
Clutch-brake systems produced by Goizper are key components in cutting, forming, folding and press machines.
The aim of this presentation is to show how incorporation of the Smart G module can convert a clutch-brake system into a monitorable smart component, which includes self-diagnostics capabilities that can provide information about the current state of the component and predict failures before they occur.
Communications modules incorporated in the Smart G component provides capabilities to:
Remotely monitor the component
Send the data to a cloud platform where all historic data are stored.
Having the data of Goizper’s clutch-brakes fleet on a cloud platform will provide the possibility to use more advanced techniques and algorithms in order to predict failures and/or remaining useful life of key components of the system.
Benefits are two-fold: Goizper will drastically improve the knowledge about their equipment to improve reliability of their products and the maintenance services provided to customers, while customers will benefit from a reduced downtime of the machines and more cost-effective maintenance strategy.
Goizper and Tekniker’s work on failure prediction and diagnosis, as well as cloud platform development, have received funding from the European Union under the MANTIS project.
Figures 3 and 4. Braking and Clutching processes performance
Philips Consumer Lifestyle (PCL) is an advanced manufacturing site located in Drachten, the Netherlands. Our organization falls within the Personal Heath business cluster of Philips, and is primarily concerned with the manufacturing of personal electric shavers.
Electric shavers are comprised of two principle component ‘blocks’: a body and a shaving unit. Each shaving unit contains three metallic shaving ‘heads’, which in turn are composed of a shaving blade (the cutting element) and shaver cap (the guard). The focus of the MANTIS project at PCL falls on the production of these shaver caps.
An electro-chemical process is used in the manufacturing of shaver caps, where an electric current is passed over the raw input material, which is conductive, in order to cut this material into the desired shape. Production of the shaver caps at PCL is fully automated.
Precision tooling is required throughout the various stages of shaver cap manufacturing. At present, these tools are built on-site, and are required to be kept in stock so that replacements are available in the event of tooling malfunctions. Having functional tools available around the clock is essential to meet our goal of 100% ‘up-time’ for our assembly lines. However, this is an expensive approach to resolve the problem, both in terms of the additional equipment required and extensive down-time that results from manual tooling replacements. Therefore, the timely maintenance of these tools presents a challenge.
Currently the maintenance strategy on the production line for shaver caps is a mixture of reactive and preventive maintenance. In line with the Mantis goal, our goal is to transform this towards a predictive or even a prescriptive maintenance strategy. However, this comes with the need for data. In order to perform maintenance on the tooling at exactly the right moment needed, information is necessary about the tooling to make useful decisions.
The data directly related to the current state of tooling (e.g. degree of wear, damages, etc.) is hard to retrieve in some cases, due to process-specific reasons. In our use case the tooling is delicate and very precise (micron range, difficult geometries), which makes frequent measurements of the tooling difficult and expensive in a mass production environment. Currently, there is only indirect data available about the use of the tools in the production machines, but not about the actual state of the tool itself. These data can be used to estimate, for example, the remaining useful life of a tool, but in order to improve and verify the RUL prediction models, more direct data is necessary.
Tool wear sensor
To solve this matter, a collaboration between the University of Groningen and Philips Consumer Lifestyle has been started in context of the Mantis consortium, with the goal to develop a tool wear sensor based on an optical image system. A robust setup with a high-resolution sensor will make detailed images of the individual tools.
The raw images are preprocessed, where the parts of interest of the tool will be cut out of the image and rotated to form the input for a machine learning algorithm. Next step would be to normalize the pictures so they are more or less comparable.
Since we have no baseline, we asked our maintenance engineers (they are the domain experts) to label all these individual images. Together we choose three specific labels: wear, damage and contamination. The input of the maintenance engineers is used to train the algorithm, but also to assess how well these individual pictures are labelled similar when considering multiple engineers.
Currently, over 1500 pictures are labelled in about a month. Initial results seem to indicate that simple machine learning can outperform human labeling regarding tooling deviations.
If results are good, the trained algorithm will ultimately be used with an automatic calculation engine to run new images through the algorithm. This means that we also have to change the way of work, and provide the maintenance engineers with easy-to-use tools to make these new images, as part of their regular maintenance steps. The outcome of the analysis forms an input for determining the remaining useful life of the tool, in combination with both process and quality data.
The MANTIS Steel Bending Machine pilot aims at providing the use case owner – ADIRA – a worldwide remote maintenance service to its customers. The main goal is to improve its services by making available new maintenance capabilities with reduced costs, reduce response time, avoiding rework and allowing for better maintenance activities planning.
To this purpose existing ADIRA’s machines (starting with their high end machine model – the Greenbender) will be augmented with extra sensors, which together with information collected from existing sensors will be sent to the cloud to be analyzed. Results made available by the analysis process will be presented to machine operators or maintainers through a HMI interface.
A number of partners are involved into the development and testing of the modules, which regard the communication middleware (ISEP, UNINOVA), data processing and analytics activities (INESC, ISEP), the HMI applications (ISEP), and a stakeholder providing a machine to be enhanced with the MANTIS innovations (ADIRA).
The distributed system being built responds to a reference architecture that is composed by a number of modules, the latter grouped into 4 logical blocks: the Machine under analysis, Data Analysis module, Visualization module, and the Middleware supporting inter-module communications.
Data regarding the machine under analysis are collected by means of sensors, which integrate with the machine itself. This logical block consists thus of data sources that will be used for failure detection, prognosis and diagnosis. This set of data sources comprises an ERP (Enterprise Resource Planning) system, data generated by the machine’s Computer Numerical Controller (CNC) and the safety programmable logical controller (PLC).
This logical block operates through two basic modules. The first is the MANTIS Embedded PC, which is basically an application that can run on a low cost computer (like a Raspeberry Pi) or directly on the CNC (if powerful enough). This module is responsible for collecting the data from the CNC I/O and transmitting it to the Data Analysis engine for processing and is implemented as a communication API. When based on an external computer, this module also connects to the new wireless MANTIS sensors placed on the machine using Bluetooth Low Energy protocol (BLE). Communications are then supported by the RabbitMQ message oriented middleware, which takes care of proper routing of messages between peers. This middleware handles both AMQP and MQTT protocols to communicate between nodes.
The I/O module is used in order to extract raw information from the machine sensors which is collected by the existing PLC, made available on the Windows-based numerical controller through shared memory and then written to files. Our software collects sensor data from these files, thus completely isolating the MANTIS applications from the numerical controller’s application and from the PLC.
This logical block takes care of Data Analysis and Prediction, and it exploits three main modules. The first is a set of prediction models used for the detection, prognosis and diagnosis of the machine failures. The second is an API that allows clients to request predictions from the models, and that can respond to different paradigms such as REST or message-queue based. Finally, the third module is a basic ETL subsystem (Extraction, Transformation and Loading) that is responsible for acquiring, preparing and recording the data that will be used for model generation, selection and testing. This last module is also used to process the analytics request data as the same model generation transformations are also required for prediction.
This logical block consists of two modules, the human machine interface (HMI) and the Intelligent Maintenance DSS. The HMI is designed to be a web-based mobile application, and to be accessed via the network from any computer or tablet. The HMI is developed to work in two different modes, depending on which kind of user is accessing it. In fact, the HMI is developed to support two user types, the data analyst and the maintenance manager, allowing both of them to analyze the machine’s status, record failure and diagnostics related data. Moreover, the data analysis HMI provides an interface with the data analyst, allowing the consultation and analysis of data and results. On the other hand, the maintenance management HMI allows for consulting predicted events and suggested maintenance actions.
The second module is an Intelligent Maintenance DSS, which uses a Knowledge Base that uses diagnosis, prediction models and the data sent by sensors. On top of this Knowledge Base there is a Rule based Reasoning Engine that includes all the rules that are necessary to deduce new knowledge that helps the maintenance crew to diagnose failures.
The work performed so far is well advanced and an integration event will occur in the near future where the interconnection between all systems will be tested and validated.
The demonstrator being built, will be evaluated according to the following criteria: prediction model performance (live data sets will be compared to model generation test sets) and the applications usability (the user should access the required information easily, in order to facilitate failure detection and diagnosis).
The 1st CREMA/C2NET Industrial workshop will take place the 24th November at Orona Fundazioa facilities located in Hernani (Basque Country – Spain). The event, organised by CREMA and C2NET H2020 EU projects, is intended to present future trends of European Industry especially those related to digitalization technologies applied to manufacturing. High levels speakers from the Basque Government, the European Commission, and the Industry sector (ill give their expert vision.
Moreover, CREMA and C2NET will present findings generated in both projects highlighting their approaches to meet above challenges. Presentations and practical demonstrations will be made by partners of both projects to present innovative solutions based on digital platforms in the Cloud to boost collaboration among manufacturing companies. Advanced Cloud technologies and applications will be shown to allow manufacturing companies faster and more efficient decision making for a better use of their manufacturing assets. Different business models and exploitation strategies followed by both projects to bring their outcomes to the market will also be presented.
Some MANTIS partners such as MGEP, IKERLAN, TEKNIKER, MCC, FAGOR ARRASATE and GOIZPER will attend this event to know other EU projects approaches to deal with common research areas and to make new contacts for potential collaboration actions in the future.
Data from smart, connected devices and related external data may come in an array of formats, such as sensor readings, locations, temperatures and history. In order to understand the patterns, the spreadsheets and database tables need to be suited to user’s needs.
To capture and display maintenance-relevant information, MANTIS is designing user-friendly industry dashboards for control rooms combined with mobile extensions on the go. As MANTIS use cases vary from railway maintenance to healthcare equipment maintenance, strongly dependent on individual technical constraints, there is no common, specific MANTIS Human Machine Interface (HMI) design.
However, as central feature of most design use cases is displaying data of monitoring process and responding to alarms, parallels can be drawn and best use practices can be used in MANTIS.
E.g. presenting information such as asset maintenance history, the overall condition, sensor data and analysis was a task assigned to XLAB in several other past and current projects. We describe how feedback gained from two successful past projects – the spin off company Sentinel Marine Solutions and FINESCE smart buildings – helped draw up guidelines. Special emphasis was placed on user experience (UX), creating a relationship with the user over time and on how he feels during the whole lifecycle of the product.
The IMPACT-accelerated startup and FIWARE technology implementer Sentinel Marine Solutions follows the overall state of the boat and allows real time access to sensor data to avoid e.g. batteries losing charge. Crucial boat parameters, water level, temperature readings, battery voltage, bilge pump, are presented on a user-friendly interface – tablet, smartphone or dashboard in the control room or cabin (Image below).
Monitoring is done continuously, providing rich information about one or several vessels’ position, voltage, temperature – where ever the user may be, on land or off shore. That is why Sentinel keeps it simple, to avoid cluttering and confusion, and complements it with a visually pleasing design for sustained usage of application.
Work on previous research projects confirms that it is worth investing a significant part of the application development into how the system presents data to the end users. Within the FINESCE project, the top part of the presentation layer was designed by XLAB. The interface connects data on energy generation and consumption in smart factories and smart buildings. It had to be optimized for quick retrieval and display in order to identify deviations and enable fast responses.
The application has many views showing specific aspects of the data, but each in a context that is relevant to the workflows of end users – below an example of the Smart building consumption Overview tab.
Last but not least, the aim of the design of these applications was to present the data to the users with a pleasing appearance, while at the same time offer crucial visual information on the current and past energy consumption.
MANTIS; Cyber Physical System based Proactive Collaborative Maintenance.
This project has received funding from the ECSEL Joint Undertaking under grant agreement No 662189. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Finland, Denmark, Belgium, Netherlands, Portugal, Italy, Austria, United Kingdom, Hungary, Slovenia, Germany.