With SMARTLINK monitoring program, Atlas Copco makes use of connectivity data and data intelligence to help customer to keep up their production uptime and to improve, when possible, energy efficiency.
With approximately 100,000 machines connected with SMARTLINK, Atlas Copco makes compressors in the field communicate directly with the back office and their service technicians.
Customers become more proactive, planning is more efficient and reliability of the compressed air installations is better than ever before.
Customers of SMARTLINK get a monthly overview of machine information, including running hours and the time left before service, thus allowing them to order a service visit at the right time, maintaining maximum uptime and energy efficiency.
With SMARTLINK they can closely follow up on machine warnings via email or SMS. With this information they can take the necessary actions to prevent a breakdown.
With the MANTIS project, Atlas Copco will take proactive maintenance to the next level, by:
predicting the remaining useful life of consumables and components that are subject to wear
detecting upcoming problems or inefficiencies before they deteriorate
remotely diagnosing the root cause of an unplanned shutdown
Moreover, in order to reduce communication costs, smart sensing technology is being investigated, or how local preprocessing of information can significantly reduce the amount of data to be transmitted.
A major challenge for Atlas Copco is the huge variety of compressor types and operating conditions. To process this enormous amount of information, self-learning techniques are combined with physics-based compressor models. Eventually, these will enable the discovery of new patterns in data, collected on a worldwide scale.
The ultimate goal is to translate these data into actionable information for the global service network.
Service interventions will be planned even better and will be shorter and more efficient. Problems will be fixed in one visit, as technicians will know in advance what to do and what parts to bring.
For the customer, this means no unnecessary maintenance, less planned or unplanned downtime, therefore achieving maximum productivity.
Cyber-Physical Systems (CPS) are often very complex and require a tight interaction between hardware and software. As it happens in almost any software systems, also CPS generate different kinds of logs of the activities performed, including correct operations, warnings, errors, etc. Frequently, the logs generated are specific to the different subsystems and are generated independently. Such logs contains a wealth of information that needs to be extracted and that can be analyzed in different ways to understand how the single subsystem behaves and even retrieve information about the behavior of the overall system. In particular, considering the generated logs, it is possible to:
Analyze the behavior of a single subsystem looking at the data generated by each one in an independent way;
Analyze the overall behavior of the system looking at the correlations among the data generated by the different subsystems
Such data are very useful to understand the behavior of a system and are often used to perform post-mortem analysis when some failures happen. However, such data could also be used to understand in a more comprehensive way how the system behaves through a real-time analysis able to monitor continuously the different subsystems and their interactions. In particular, it is possible to focus on preventing failures through predictive maintenance triggered by specific analysis.
Making predictions about system failures analyzing log files is possible but such predictions are strictly related to some characteristics of such files. In particular, some very important characteristics are: data generation frequency, information details, history.
The data generation frequency needs to be related to the prediction time and the time required to take proper actions. For example, if we need to detect a failure and take proper action in a few minutes, we need to use data generated with a higher frequency (e.g., in the scale of the seconds) and we cannot use data generated with a lower one (e.g., in the scale of the hours). This requirement affects the ability to make predictions and their usefulness to implement proper maintenance actions.
The information details provided need to include proper granularity and meaningful massages. In particular, it is important to get detailed information about errors, warnings, operations performed, status of the system, etc. The specific details required are tightly connected to the specific predictions that are needed. Moreover, the finer the granularity of the information, the higher are the chances of being able to create a proper prediction model.
High quality data history is required to build proper prediction models. However, just having a large dataset is not enough. Historical data need to be representative of the operating environment and include all the possible cases that may happen during operations. In particular, it is required to have information about the log entries and the actual behavior of the system to create a reliable model of the reality.
The requirements described are just a first step towards the definition of a proper predictive maintenance model but they are essential. Moreover, the proper approaches and algorithms need to be selected based on the specific system and the related operating conditions.
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 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:
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.
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.
Industrial sectors such as automation, energy and mechanical manufacturing are more increasingly in the need of including predictive maintenance techniques and processes, in order to improve their product quality and reduce their maintenance costs.
In such scenarios, fatigue is a cumulative phenomenon that appears when material is subjected to repeated loading and unloading. When this happens and the target is stressed beyond its critical threshold, microscopic cracks begin to form, which will eventually end fracturing the structure. Thus, smart sensors must be placed in the critical zones, in order to early monitor the growth and evolution of the cracks.
As it is described within WP3’s use case 1.3 in MANTIS project, which focuses on developing a framework for smart sensing and data acquisition technologies, there are several detection techniques for direct crack measurements, such us regular strain gauges and crack gauges. These last elements are made of aligned grids which are disconnected one by one with the propagation of the crack. The drawback comes in terms of placement, as the location of the failure is not always predictable and therefore it would require a complex multi-gauge installation, in order to cover a large area of sensorization and anticipate the formation of cracks.
Another approach which is being analyzed and tested within use case 1.3, is the utilization of conductive inks, which could give more flexibility in terms of placement and the design of the sensorization area to be monitored.
If the fissure detection is performed with conductive inks a necessary requirement must be taken into account. As the structures that are monitored are electrically conductive, an insulation layer must be deposited between the structure and the conductive ink. For the efficient detection of fissures, this insulation layer should break with the structure, but it must not brittle with time, temperature, humidity, etc. Even more, it should be easily deposited, as the structure may be located in a difficult to access place.
According to the conductive ink, it should also be of easy deposition, with low resistivity and withstand high temperatures without breaking.
In the preliminary tests, a Magnesia paste based insulation layer has been used and on top of it, a vinyl mask layer has been stuck for the definition of the conductive layer structure. As conductive ink, a low resistivity silver ink has been used, defining two structures: a gage structure, Figure 1, and continuous conductive line.
During the fissure measurements, the gage structure has been supplied and measured, recording both current and voltage. As it is shown in Figure 2, as the lines of the gage structure break, an increase in the measured voltage and resistance is recorded.
Thus, properly deposited and structure adapted conductive inks could stand as a solution for the early detection of fissures in industrial structures.
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.