The continuous and systematic analysis of performance data from the monitoring of operational PV power plants is vital to improving the management and thus profitability of those plants over their lifetime. The article draws on an extensive programme undertaken by 3E to assess the performance of a portfolio of European PV power plants it monitors. The article illustrates 3E’s approach to automatic fault detection. It will explore the various data mining methodologies used to gain an accurate understanding of the performance of large-scale PV systems and how that intelligence can be put to the best use for the optimal management of solar assets.
3E’s work on automatic fault detection and diagnosis has received funding from the European Union under the MANTIS project.
Xtel Wireless is a Danish company specialized in ultra-low power solutions and technologies. With a specialized knowledge and competences in developing state-of-the-art IoT (Internet of Things) products and wireless core technologies, Xtel Wireless has an expertise in developing innovative products. Xtel Wireless employs 15 engineers with an average experience of 15 years in developing high-tech and embedded solutions, as well as having design and innovation competences in the team.
Relevant Expertise for the project:
Xtel Wireless has an expertise in developing innovative products. In the IoT business segment, Xtel Wireless has developed a platform for wireless sensor modules. This platform has unique features in matter of performance, price level and size.
Role in the project:
Xtel Wireless will be involved in developing sensors for typical domestic households to provide the project with data, monitoring humidity, CO2-levels, temperature and VTO in the residential buildings. The aim is to collect and distribute the data, as well as present the data motivationally to the residents hereby encouraging changed energy behavior.
Sataservice (Group) Oy provides industrial maintenance services and projects for customers to reach high level of performance. The Group includes following companies, Sataservice Oy and Rauman Sähkökonehuolto Oy.
Both companies have perfect service portfolio and co-operation. Sataservice was founded in 2003. The headquarter is in Rauma, Finland. The company has been growing every year and for the moment we have approximately 350 employees. The turnover 2016 is around 35 M€. We are ISO 9001:2008 certified by Bureau Veritas.
The company have expertise of demanding environments which means that mainly our customers are in businesses that have demanding regulations and laws that have to be followed, for instance the food industry where we are involved with living animals and in the end of the process we handle ready made food. We are also involved in the medical business with its own regulations. We mainly have customers that have outsourced their maintenance, either production or facility maintenance or both. We do also lot of projects such as modernizations of old equipment that ensures that the lifecycle can be extended and the productivity is high for the customer.
We have expertise in mechanical, electrical, automation, HVAC and cranes. Rauman Sähkökonehuolto is specialized in electrical motors, pumps and gears and helps us to keep up the customers productivity, while we can manage ourselves most of all maintenance areas and can minimize unnecessary waiting time. At the moment most of our customers are in Finland but we are aiming to expand abroad when we find the right partners and customers.
Relevant Expertise for the project:
We have a really broad expertise in maintenance of production equipment, especially machining, packing machines, electrical motors, automation and so on. We have divided our business in different technologies and all of those have a dedicated leader for instance for cranes, HVAC and automation.
Role in the project:
We have a good possibility to provide environments to test use-cases. We want to develop ourselves our IoT and CBM strategy and clarify what we can and should offer our customers. The aim is that everyone, meaning our customers, ourselves and possibly third parties, sees it as a win-win situation. We also want to find good long term partners to execute the strategy.
Neogrid Technologies ApS, is an entrepreneur company working with intelligent energy visualisation, monitoring and control, utilising knowhow within wireless communication technology to develop Smart Grid solutions.
Neogrid Technologies develop intelligent forecast based energy management systems for both consumers, energy companies and 3rd party actors, enabling home energy management capabilities for house owners and large scale monitoring – allowing optimised individual and aggregated controlling.
This consists of a data acquisition and IoT platform and online control-interface. It includes advanced analytics, which are able to forecast energy consumption and flexibility, based on individual house modelling and advanced model predictive control.
The aim is to enable the user to monitor, plan and even shift energy consumption based on knowledge of price, average consumption patterns and conditions, allowing for cost reduction and increased control and overview. The integration between hardware and software goes well with the background and experience of the people behind Neogrid Technologies.
Furthermore, Neogrid Technologies develop and deliver a cloud-based system for monitoring and optimized individual and aggregated control of heat pumps and district heating buildings based on requirements from the energy system actors and 3rd party actors.
Relevant Expertise for the project:
Neogrid Technologies have by participation in selected research project gained more than 6 years’ experience developing control strategies and cloud-applications for heat pumps and district heating focusing on the power and district heating system and end user requirements. Through this work, Neogrid Technologies have gained deep knowledge and experience regarding practical challenges arises when 80 live heat pump installations are centrally controlled.
Role in the project:
Neogrid will Participate in WP7, task 7.3, where an integrated energy system comprising of energy production, distribution and energy consumption of buildings (electricity and district heating) can be forecasted, optimized and moved according to various needs in the energy system.
Via aggregated control of a number of buildings (district heating and heat pumps), besides what is mentioned above, the predicted energy consumption can be taken into account when planning maintenance outage/interruptions so the influence or disadvantage of the heating service towards the consumers is minimized.
The above mentioned IoT platform Neogrid will develop and deliver services where the Product PreHeat and aggregator among other will be used to simulate and analyze the energy system with respect saving energy, monitoring and predicted maintenance.
The MANTIS project is concerned with predictive maintenance on the basis of big data streams from large (industrial) operations. At the end of the processing pipe line, planning suggestions for maintenance actions will be the result. Usually, maintenance is performed by human operators.
However, with current developments in machine learning, AI and robotics, it becomes interesting to see what type of ‘corrective actions’ in maintenance could be performed by industrial service robots.
In industrial production lines it is common to observe fairly short times between failure, especially in long chains. Whereas individual components are often designed to function extremely well, for instance under a regime of ‘zero-defect manufacturing’, the performance of the line as a whole may be disappointing. What is more, the actions performed by human operators to solve the problems may be very mundane and simple, such as removing dirt due to fouling or lubricating critical components. With the current advances in robot hardware and software technology, it becomes increasingly attractive to automate such maintenance actions. Whereas maintenance in the form of module- or part replacement are too difficult for current state-of-the-art robotics, cleaning and tidying is definitely possible.
With this application domain in mind, a laboratory setup was designed for quickly developing a robotic maintenance task for the purpose of demonstration by a master student team (Francesco Bidoia, Rik Timmers, Marc Groefsema) under guidance of a PhD student (Amir Shantia). We were able to realize a rapid configuration of our existing mobile robot platform to realize simple cleaning and tidying actions, similar to what is needed in basic industrial maintenance tasks. The demonstration involves speech control, navigational autonomy, work piece approach and dynamic reactivity to three object types, using tool switching. Objects are considered to be either a) untouchable, or b) removable by hand, or to consist, c) of small fragments (cf. ‘dirt’) that needs to be brushed away. In three weeks, a full demonstration could be developed by the student team, using a mobile robot with a single arm that was designed earlier, for Robocup@Home tasks:
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.
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 (email@example.com)
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
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.
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.