On October 24th Sirris organized an industrial seminar on the opportunities and challenges related to fleet-based data exploration. During this seminar, a general introduction to the MANTIS project was first given, followed by presentations from several partners within theMANTIS project including: the Mondragon University (press machines), the Eindhoven University of Technology (shaver manufacturing), 3E (Photovoltaic Plants), Ilias Solutions (Vehicles), Atlas Copco (compressors) and Sirris. The event was a real success with around 45 participants and offered participants via real-world use cases in the different industrial domains mentioned above the opportunity to see how data-driven analytics on a fleet of machines can optimize the operation and maintenance of those.
If you would like to have further information on the outcomes of this seminar, please contact Caroline Mair (firstname.lastname@example.org)
There are two extreme approaches to predicting failures for predictive maintenance. The white box approach relies on manually constructed physical and mechanical models for predicting the failures. The black box approach, on the other hand, relies on failure prediction models constructed using statistical and machine learning methods based on the data gathered from a running system. The figure below illustrates such data driven failure prediction for a machine monitored by three sensors.
Machine learning algorithms are used to identify failure patterns in the sensor data that precede a machine failure. When such patterns are observed in operation, an alarm can be triggered to take corrective action to prevent or mitigate the eminent failure. For example, failure predictions can be used to optimize the maintenance actions, such as scheduling the service engineers or managing the spare parts storage to reduce the downtime cost.
Automatic feature extraction
An important part of modeling a failure predictor is selecting or constructing the right features, i.e. selecting existing features from the data set, or constructing derivative features, which are most suitable for solving the learning task.
Traditionally, the features are selected manually, relying on the experience of process engineers who understand the physical and mechanical processes in the analyzed system. Unfortunately, manual feature selection suffers from different kinds of bias and is very labor intensive. Moreover, the selected features are specific to a particular learning task, and cannot be easily reused in a different task (e.g. the features which are effective for predicting failures in one production line will not necessarily be effective in a different line).
Deep learning techniques investigated in the MANTIS project offer an alternative to manual feature selection. It refers to a branch of machine learning based on algorithms which automatically extract abstract features from the raw data that are most suitable for solving a particular learning task. Predictive maintenance can benefit from such automatic feature extraction to reduce effort, cost and delay that are associated with extracting good features.
On October 24th Sirris is organizing in Belgium an industrial seminar on the opportunities and challenges related to fleet-based data exploration. During this event Belgian as well as other European industrial partners from the MANTIS project will present their experience with fleet-based analytics (based on use-cases from the MANTIS project).
Many companies operate a fleet of machines that have a similar, almost identical behaviour in terms of internal operation, application and usage, such as for example windmills, compressors and professional vehicles. This set of almost identical machines is defined as ‘a fleet’.
In addition, more and more, those machines are equipped with several (smart) sensors, that can capture data on operational temperature, vibrations, pressure and many other features, depending on the machine. In addition, the communication and data storage technologies are becoming ubiquitous, making it possible to gather the data in a central platform and derive insights into normal and anomalous behaviour across the entire fleet of machines. By comparing for example the behaviour of a single machine to the rest of the fleet, one can identify if a machine is underperforming due to misconfiguration or imminent failure. The analysis of this data can also help service and maintenance personnel to have a more detailed and optimised maintenance planning, e.g. ensuring an optimal distribution of the entire fleet in terms of remaining useful life, in order to manage the work load of the service engineers. Therefore, the exploitation of the data collected on a fleet of machines is a real asset for maintenance and service personnel and, at a larger scale, for an entire company.
You are interested in this event? Check out the event’s agenda and register here
13:00 – 13:15: Registration and coffee
13:15 – 13:30: Setting the scene (Sirris)
13:30 – 14:30: MANTIS project: Cyber Physical System based Proactive Collaborative Maintenance
Project goals and challenges by Sirris
Fagor use case (press machines) – title to be announced
Philips use case (shaver manufacturing) – title to be announced
14:30 – 15:30: Root cause analysis
Barco – Vitriol: let open source data science talk quality and business at Barco Projection
3E – Data-driven Fault Detection for Photovoltaic Plants: Data Quality, Common Faults and Data Annotation
Atlas Copco – SMARTLINK & root-cause analysis on compressors worldwide to improve on operational efficiency
One of the most important tasks to ensure flawless working on different work packages is to have fully consolidated requirements towards the MANTIS platform. Not only is it important to have all different requirements aggregated, but also to know in which project tasks those requirements will be addressed, by whom and how to manage those during the project lifetime. To handle this challenge with excellence, MANTIS partners decided to do a 4-step approach in requirement definition towards the MANTIS platform.
Defining User Scenarios & Deriving Requirements
In the MANTIS project, we do have specific use cases we later on want to test the platform against. Each of these use cases detailed their scenarios and related them to the MANTIS objectives. From there on, requirements in the form of a table were compiled. Those requirements cover functional, non-functional, technological and business needs. As a result, we had the first form of requirements which – of course- still needed unification and consolidation.
Extending Maintenance Use Case Requirements by MANTIS Partner Requirements
This task focused on what technology can and should be used for the user scenarios. The main task members therefore were the technology providing partners. We also expected requirements originating from technology push of the providers. Those additional requirements were the second form of requirements we had which – again, of course – still needed unification and consolidation. Nonetheless, correlations to the user scenario requirements were already identified and noted.
Requirements and Project Objects Consolidation
The platform requirements and the user scenario requirements were revisited for consistency and updates. Furthermore, a refinement of the requirements was performed based on the early sketches of the MANTIS architecture. As a result, 45 different requirement categories were identified, 27 of those categories were then identified as “not to be addressed inside of MANTIS”, since those categories mainly were basic requirements towards a general platform architecture and not MANTIS specific.
Refinement of the Requirements
As the last task, which is currently still running strong, we wanted to match all those 900+ requirements that were identified and defined to the 45 defined requirement categories. At the time of writing this article, the matching is completed and 900 requirements could be reduced to about 330 MANTIS specific requirements. Also, the MANTIS specific requirement categories were matched to the different project task, to make sure that the requirements will definitely be addressed.
So what’s ahead: Definition of MANTIS platform requirements is near its end. What’s left to do is to choose a good way to manage the requirements during projects lifetime. As it seems, this will be done by separating Excel sheets according to tasks and categories. This will keep management of requirements handy and easy.
Off-Road and Special Purpose vehicles are used all over the world in various environmental conditions. They exist in all different kinds and formats and, within companies, a broad range of types of such vehicles will be in use.
Maintenance on these vehicles, be it preventive or corrective, can cause unavailability, having a negative impact on both productivity and efficiency. An overall objective regarding maintenance is to maximize the availability of the vehicles at the lowest maintenance cost. Therefore, a pro-active and preventive maintenance approach should lead to important savings, with higher availability.
The ILIAS approach
Most of these vehicles are already equipped with on-board HUMS systems/black boxes. The data generated by these on-board systems contain a broad range of information that can be used as input in a MANTIS-based platform in order to optimize the full maintenance strategy.
The diversity of HUMS systems, however, is very broad, even on the same type of vehicles. Each vehicle has its own configurations, interfaces, data formats, etc. Hence, there is a need to convert the collected data from various systems into a uniform and structured format in order to make them further exploitable.
This observation has led us to the conclusion that there are two viable approaches to building MANTIS-based platforms:
A per-vehicle type / HUMS system platform approach, aiming at an optimum maintenance strategy for a small number of equipment types.
An open platform approach that can be easily customized by the user to the type of vehicle/HUMS system(s) being used.
We opted for the second approach but have not limited it to the collection of data only but broadened it to a complete set of functionalities within the MANTIS-based platform.
Based on the experience and know-how gained from the collaboration within the MANTIS project and the architectural guidelines derived from it, ILIAS Solutions aims at building a platform that provides a complete solution from the readout of the black box until the optimization of the maintenance plans in an environment with high numbers of highly complex and mobile assets.
The ILIAS platform, therefore, provides users with an integrated set of user-friendly tools, permitting them to:
Import data from external sources like ERP systems, leading to a centralized data set. (Step 1)
Import raw data coming from any kind of HUMS system and cleanse them, based on automatic data wrangling, leading to state detection and health assessment. (Steps 2, 3, 4)
Make analysis of the data via different algorithms and translate them into rules/conditions to apply in the system. (Steps 5)
Define rules/conditions, including use and abuse rules, for triggering maintenance or other linked actions, based on the combined dataset. (Step 6)
Approve/disapprove the system-proposed maintenance actions and register them to make the system self-learning. (Steps 7, 8, 9)
This figure illustrates the approach.
The figure below illustrates how we go through different steps in implementing the platform, following more iterations to improve the system.
For Off-Road and Special Purpose vehicles, the overall objective regarding maintenance is to maximize the availability of the vehicles at the lowest maintenance cost. Thus, a proactive and preventive maintenance approach leads to important savings, with higher availability.
ILIAS Solutions aims at building a platform that provides a complete solution from the readout of the black box until the optimization of the maintenance plans in an environment with high numbers of highly complex and mobile assets.
This platform should be an open platform that can be easily customized by the user to the type of vehicle/HUMS system(s) in use and where a number of rule sets/conditions are defined in a user-friendly way. This allows the system to trigger predictive maintenance actions. Analysis of broad data sets will lead to additional rules and conditions, optimizing the platform it selves.
T-Rex project – Lifecycle Extension Through Product Redesign And Repair, Renovation, Reuse, Recycle Strategies For Usage & Reusage-Oriented Business Models
In current global economy, manufacturers are under pressure to adapt to an ever-changing business environment. As a consequence, as part of Industry 4.0, new trends are gaining momentum, such as the servitization of manufacturing. The servitization can also be seen as a business model innovation of organization process and capabilities, where service-oriented activities increase. This leads in turn to revise the importance of certain strategies and technologies, such as reliability and life-cycle assessment, service engineering or advanced maintenance.
Previous to the MANTIS project, during last 3 years, T-REX project, funded under 7th framework factories of the future programme, has developed technologies oriented to the extension of machinery life-cycle, component re-use and servitization. Moreover, it has developed a framework and other support tools to facilitate new business opportunities to companies, in particular SMEs.
These activities are all contributing towards the development of Industry 4.0, in particular with respect to the extension of the manufacturing activities beyond the factory.
Workshop “Industry 4.0: Extension of machinery life-cycle, component re-use and servitization”
As closure event for the T-Rex project in September 2016 IK4-TEKNIKER and ULMA are organizing a workshop which aims to demonstrate the feasibility of service-oriented business models, in particular for SMEs, and will include direct feedback from manufacturing companies interested in extending servitization and re-use activities. Part of these activities will take advantage of the results obtained in T-REX project.
Science and Technology BV (S[&]T) is a SME developing cutting edge technology for complex systems. The company has built track record in space industry and since several years it is applying its innovation knowledge in smart manufacturing. Examples are central use of predictive analytics for real-time optimization and automated event handling with intelligent database and self-learning algorithm enabling impact analysis and decision support.
Below are some examples of previous projects that helped build the knowledge base:
Decision Support Systems
Time is a scarce and expensive resource aboard challenging scientific environments such as the International Space Station (ISS). The training and preparation of astronauts for onboard missions can take up a large amount of this resource, as do the actual maintenance, operation and troubleshooting involved with such missions. ESA has been researching more efficient, effective, and easy ways to realize human operations. S[&]T works on the following projects: ETECA (Expert Tool to Enhance Crew Autonomy), MECA (Mission Execution Crew Assistant) and TIDE (Technology for Information, Decision and Execution superiority). These technologies form the basis of decision support development in MANTIS.
System Health Management
SHM optimizes operation of complex systems by analyzing their health using either model-based methods i.e., using online sensor data and knowledge of normative system behaviour, or data-driven techniques, i.e., system behaviour is learned and extracted from large data volumes. Typical functionality includes: fault detection, diagnosis of system failures, and prediction of system failures. S[&]T has been involved in: ESA’s Future Launchers Preparatory Programme (FLPP), the real-time multiple sensor array LOFAR, and the development of a health management system for a Reusable Space Transportation System.
Aim of S[&]T is to further develop its digital factory tools in a modular way. In this way specific analysis- and decision-support solutions can be created, dedicated per client.
Internet of Things Applications in Future Manufacturing
The book chapter “Internet of things Applications in Future Manufacturing” is part of the 2016 IERC-European Research Cluster on the Internet of Things book (Digitising the Industry – Internet of Things Connecting the Physical, Digital and Virtual Worlds). This book is published by “River Publishers Series in Communications” that is a series of comprehensive academic and professional books which focus on communication and network systems. The book chapter is the result of a collaboration between John Soldados (Athens Information Technology), Sergio Gusmeroli (Politecnico di Milano) and MANTIS partner: Pedro Malo and Giovanni Di Orio (UNINOVA). You can download the book chapter in the Dissemination section of the web as well as the entire book by using the following link:
Introduction — Future manufacturing is driven by a number of emerging requirements including:
The need for a shift from capacity to capability, which aims at increasing manufacturing flexibility towards responding to variable market demand and achieving high-levels of customer fulfillment.
Support for new production models, beyond mass production. Factories of the future prescribe a transition from conventional make-to-stock (MTS) to emerging make-to-order (MTO), configure-to-order (CTO) and engineer-to-order (ETO) production models. The support of these models can render manufacturers more demand driven. For example, such production models are a key prerequisite for supporting mass customization, as a means of increasing variety with only minimal increase in production costs.
A trend towards profitable proximity sourcing and production, which enables the development of modular products based on common plat- forms and configurable options. This trend requires also the adoption of hybrid production and sourcing strategies towards producing modular platforms centrally, based on the participation of suppliers, distributors and retailers. As part of this trend, stakeholders are able to tailor final products locally in order to better serve local customer demand.
Improved workforce engagement, through enabling people to remain at the heart of the future factory, while empowering them to take efficient decisions despite the ever-increasing operational complexity of future factories. Workforce engagement in the factories of the future is typically associated with higher levels of collaboration between workers within the same plant, but also across different plants.
The advent of future internet technologies, including cloud computing and the Internet of Things (IoT), provides essential support to fulfilling these requirements and enhancing the efficiency and performance of factory processes. Indeed, nowadays manufacturers are increasingly deploying Future Internet (FI) technologies (such as cloud computing, IoT and Cyber-Physical Systems (CPS) in the shop floor. These technologies are at the heart of the fourth industrial revolution (Industrie 4.0) and enable a deeper meshing of virtual and physical machines, which could drive the transformation and the optimisation of the manufacturing value chain, including all touch-points from suppliers to customers. Furthermore, they enable the inter-connection of products, people, processes and infrastructures, towards more automated, intelligent and streamlined manufacturing processes. Future internet technologies are also gradually deployed in the shopfloor, as a means of transforming conventional centralized automation models (e.g., SCADA (Supervisory Control and Data Acquisition), MES (Manufacturing Execution Systems), ERP (Enterprise Resource Planning)) on powerful central servers) towards more decentralized models that provide flexibility in the deployment of advanced manufacturing technology.
The application of future internet technologies in general and of the IoT in particular, in the scope of future manufacturing, can be classified in two broad categories:
IoT-based virtual manufacturing applications, which exploit IoT and cloud technologies in order to connect stakeholders, products and plants in a virtual manufacturing chain. Virtual manufacturing applications enable connected supply chains, informed manufacturing plants comprising informed people, informed products, informed processes, and informed infrastructures, thus enabling the streamlining of manufacturing processes.
IoT-based factory automation, focusing on the decentralization of the factory automation pyramid towards facilitating the integration of new systems, including production stations and new technologies such as sensors, Radio Frequency Identification (RFID) and 3D printing. Such integration could greatly boost manufacturing quality and performance, while at the same time enabling increased responsiveness to external triggers and customer demands.
Within the above-mentioned categories of IoT deployments (i.e. IoT in the virtual manufacturing chains and IoT for factory automation), several IoT added-value applications can been supported. Prominent examples of such applications include connected supply chains that are responsive to customer demands, proactive maintenance of infrastructure based on preventive and condition-based monitoring, recycling, integration of bartering processes in virtual manufacturing chains, increased automation through interconnection of the shopfloor with the topfloor, as well as management and monitoring of critical assets. These applications can have tangible benefits on the competitiveness of manufacturers, through impacting production quality, time and cost. Nevertheless, deployments are still in their infancy for a number of reasons including:
Lack of track record and large scale pilots: Despite the proclaimed benefits of IoT deployments in manufacturing, there are still only a limited number of deployments. Hence manufacturers seek for tangible showcases, while solutions providers are trying to build track record and reputation.
Manufacturers’ reluctance: Manufacturers are rather conservative when it comes to adopting digital technology. This reluctance is intensified given that several past deployments of digital technologies (e.g. Service Oriented Architectures (SOA), Intelligent Agents) have failed to demonstrate tangible improvements in quality, time and cost at the same time.
Absence of a smooth migration path: Factories and production processes cannot change overnight. Manufacturers are therefore seeking for a smooth migration path from existing deployments to emerging future internet technologies based ones.
Technical and Technological challenges: A range of technical challenges still exist, including the lack of standards, the fact that security and privacy solutions are in their infancy, as well as the poor use of data analytics technologies. Emerging deployments and pilots are expected to demonstrate tangible improvements in these technological areas as a prerequisite step for moving them into production deployment.
In order to confront the above-listed challenges, IoT experts and manufacturers are still undertaking intensive R&D and standardization activities. Such research is undertaken within the IERC cluster, given that several topics dealt within the cluster are applicable to future factories. Moreover, the Alliance for IoT Innovation (AIOTI) has established a working group (WG) (namely WG11), which is dedicated to smart manufacturing based on IoT technologies. Likewise, a significant number of projects of the FP7 and H2020 programme have been dealing with the application and deployment of advanced IoT technologies for factory automation and virtual manufacturing chains. The rest of this chapter presents several of these initiatives in the form of IoT technologies and related applications. In particular, the chapter illustrates IoT technologies that can support virtual manufacturing chains and decentralized factory automation, including related future internet technologies such as edge/cloud computing and BigData analytics. Furthermore, characteristic IoT applications are presented. The various technologies and applications include work undertaken in recent FP7 and H2020 projects, including FP7 FITMAN, FP7 ProaSense, ECSELMANTIS, H2020 BeInCPPS, as well as the H2020 FAR-EDGE initiative. The chapter is structured as follows: The second section of the chapter following this introduction illustrates the role of IoT technologies in the scope of EU’s digital industry agenda with particular emphasis on the use of IoT platforms (includ- ing FITMAN and FIWARE) for virtual manufacturing. The third section is devoted to decentralized factory automation based on IoT technologies. A set of representative applications, including applications deployed in FP7 and H2020 projects are presented in the fourth section. Finally, the fifth section is the concluding one, which provides also directions for further research and experimentation, including ideas for large-scale pilots.
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.
A large proportion of the costs of the European railway Infrastructure is related to maintenance and the current situation of railway maintenance .
The increasing usage of the railway infrastructure, due to a growing frequency of passenger and freight trains on it, and environmental and safety regulations, the increasing of maintenance requirements cannot be met without a substantial shift in maintenance strategies.
In this prospective, the new “Proactive/Predictive Maintenance” approach will involve/have an impact all the main railway stakeholder on long-term preservation of assets (expressed in RAMS requirements) minimizing life cycle costs, while for the railway operators, the proactive maintenance will improve the railway infrastructure availability and reliability.
Switches and Crossings (S&C) are fundamental infrastructure assets that allow efficient routing of trains on the network. Assessing their current status and ensuring their proper functionality is obviously a key requirement to guarantee the railway infrastructure availability for the railway operators. For these reasons, Ansaldo STS tries to discover in the Mantis project, references concerning railway S&C condition monitoring based on a wide range of methodologies from the world of statistics, data mining, time series analysis, machine learning, and filtering.
Given the examined literature, it seems clear that the use of the word “nowcasting” can be associated with: a shorter timeframe respect to “forecasting”, a different approach or algorithm for performing the estimation of the value of interest, the fact that the data used for the estimation is imprecise, uncertain, incomplete or is only indirectly related to the phenomenon of interest, and, finally, with the purpose of providing an alert for a sudden event or a possible anomaly.
A reasonable simple definition could be adopted in the framework of the Mantis project for differentiating “forecasting” and “nowcasting” processes:
The process of exploiting past and present data to make deductions about the future.
The process of exploiting past and present uncertain or incomplete data to make deductions about the present.
Many of the failures are not detected until the asset is being operated by the interlocking system when trying to lock the train route. This means that for some failures, nowcasting cannot be done before operating the unit. Frequent test procedures could solve this issue but it has other disadvantages like the introduction of increased wear, cost and reduced inherit capacity. Another solution for this problem is to introduce additional condition monitoring systems for detection of different states of critical components in the S&Cs.
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.