Usage of the MANTIS Service Platform Architecture for servo-driven press machine maintenance
A forming press, is a machine tool that changes the shape of a workpiece by the application of pressure. Throughout MANTIS project, FAGOR ARRASATE’s servo-driven press machine is being analysed, in order to set up strategies that will permit to carry out an online predictive maintenance of the press machine.
The proposed solution advocates for soft sensor based algorithms. The soft sensing algorithms provide information about the physical status of the components, as well as information about the performance of the systems. These algorithms take advantage of existing or available internal signals of the systems. The objective is to estimate inaccessible states and parameters of the systems using as few physical sensors as possible to acquire the necessary signals to work with.
Currently a characterization of the system components has been done, in a scaled test bench of real press machine. A servomotor has been analysed in order to extract information about its performance during press machine work cycles, such as the applied current, voltages and generated torque. Besides, the applied soft sensor algorithm has proven to be suitable for estimating the desired magnitudes of systems when some of the system parameters are unknown.
At the same time, the mechanical part of the press machine has been analysed in order to elaborate an analytical model of the mechanical part of the system. The purpose of this development is to relate the torque generated by the servomotor with the force applied by the press ram.
This information will be used to detect effects that occur during metal forming processes, such as unbalanced forces and the cutting shock effect, allowing to carry out the maintenance of the system.
Wireless communications are used in many industrial maintenance scenarios and are practically the only choice for transmitting data from rotating sensors. One such example in MANTIS is the shaft-mounted torque sensor in the press machine by FAGOR ARRASATE, shown below. The sensor sends the data to the receiving antenna mounted vertically from the machine’s ceiling.
The wireless signal must travel from transmitting antenna to the receiving one without overly high attenuation. Furthermore, the signal can travel via different paths, such that out-of-phase components attenuate each other. This so-called multipath effect is particularly strong in industrial environments that contain many large metallic surfaces. Correct placement of the antennas is therefore crucial. A good placement can often be found experimentally through trial and error. However, in certain cases where repeatedly re-locating a receiver or transmitter is not practical, a numerical simulation of radio wave propagation can be used instead.
As an illustration of concept, we present here a simulation of antenna placement for a simplified model of a part of press machine, shown below. The orange downward-pointing arrow shows the placement of the sensor and the orientation of its transmitting antenna. The white-and-gray shaded rectangle above the sensor is the receptor plane with the result of the simulation, as will be explained below. Note that the model is enclosed in a rectangular box from all six sides, but the front and top sides are not shown here in order to be able to see inside.
The simulation algorithm is based on the ray tracing method-of-images enhanced by double refraction modeling. It is computationally complex but highly parallel, and has thus been adopted to run on GPUs. In our case the runtime of a single simulation is approximately one minute on a high-end gaming GPU card. The simulator itself was developed by the Jožef Stefan Institute as part of the national research project ART (Advanced Ray-Tracing Techniques in Radio Environment Characterization and Radio Localization), co-funded by the Slovenian Research Agency and XLAB.
In order to use it in MANTIS, XLAB has developed a Blender plug-in that exports the model into the proprietary simulator format, and a similar import plug-in to import back the simulation results. We then ran a series of experiments simulating the rotation of the shaft, thereby changing both the position and the orientation of the transmitter antenna. The signal wavelength was 0.1225 m, corresponding to Bluetooth/WiFi frequency range. The video below shows the result. The color scale is from 0 dB loss (black) to 100 dB (white). However, values over 90 dB are replaced by red to highlight the areas that will most probably not have acceptable reception with common BLE or WiFi antenna setups.
Clearly visible are the vertical belts resulting from the obstruction by and reflections from the shafts and the diagonal patterns of reflections from the slanted parts in the model. Most importantly, within the belts of good reception we can see strong multipath interference patterns. Some of the individual, isolated red and white points are artifacts of the simulation where incidentally no ray has reached that exact area. These artifacts could be reduced by increasing the number of cast rays, which would also slow down the simulation considerably. Finally, it has to be noted that this experiment was only intended as an illustration of concept and no validation or comparison to actual signal measurement in the field was performed yet.
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:
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.