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.
Limit checking of measured variables in a monitored system is a method frequently used for fault detection. 3E uses it as a first step on its protocol of fault detection and diagnosis to know at which stage of a Photovoltaic plant actions need to be taken before any further deep analysis on the characteristics of the problems. Here, 3E illustrates the methodology used to apply it in their use case.
Photovoltaic (PV) plants are energy conversion systems which convert sunlight into electricity that is fed into the public utility grid. The physical structure and the important process variables of a PV plant measured when monitoring the performance of a photovoltaic (PV) plant are illustrated in Figure 1. The input variables of the process model are: the solar irradiance in the plane of the PV array (GPOA) and the ambient temperature (Tamb). Output variables from the process model point of view are: the PV module temperature (Tmod); the Direct Current voltage(VDC) and current (IDC) at the output of the PV array; the Alternating Current voltage (VAC); the power factor (PF); and the electric AC power to the grid (PAC).
Normalized performance parameters can be derived from the previously mentioned measurements and allow to quantify the energy flow and losses through the PV array per loss type. They are:
LA,V = YA,T – YA
with LA,I, LA,T, LA,V, the conversion losses due to current, temperature and voltage, respectively and Yr, YA,I, YA,T, YA the normalized energy yield from reference yield (based on irradiation from the sun), array yield after current losses, array yield after temperature losses and array yield after all array losses, respectively.
The main variables used for limit checking are solar irradiance in the plane of the PV array (GPOA), ambient temperature (Tamb), PV module temperature (Tmod), DC voltage and current at the output of the PV array (VDC, IDC) and electric AC power to the grid (PAC). The AC voltage (VAC) and power factor (PF) are not used for limit checking.
For checking the operational performance over different energy conversion steps, a performance loss ratio per step is defined. This performance loss ratio is computed for a given time span, e.g., a day up to several months. It is the useful energy lost over the energy conversion step divided by the energy available, i.e. the incoming solar energy on the PV array as represented by the solar irradiance in the plane of the PV array (GPOA); all normalized to standard rating conditions of the PV array. Accordingly, the overall performance of a PV plant is described by the performance ratio (PR), i.e., 100% minus the sum of all performance losses.
In practice, we compare the performance loss ratios from measurements to model-based performance loss ratios and thresholds. The model is fed with measured values of GPOA and Tamb. The model parameters can be set from data sheet parameters of the devices in the PV plant or identified from measurements from the plant in a healthy state. Accordingly, adequate limits can be derived either from tolerances on the data sheet parameters or from choosing percentiles from the healthy plant. Both the model-based performance loss ratios and their limit values vary depending on the PV plant and the weather during the evaluation period.
Figure 2 illustrates this application of limit checking for a PV plant located in Belgium. The current-related array losses (‘Array (current)’) in Figure 2a by far exceed the threshold. During a thorough maintenance action after this problem was detected, several smaller PV module failures were fixed. After maintenance action, all performance loss ratios were back within their expected ranges, yielding a much higher PR of 82.9% (Figure 2b).
In the frame of Mantis, the three Belgian partners (Sirris, Ilias Solutions and 3E) focused their work on exploiting intelligent data-driven technologies for failure detection and root-cause analysis. In this video we will show the work done and the results obtained within the Mantis project by the Belgian consortium:
This use case studied the analysis of sensor data from a brake press in order to facilitate its maintenance. Brake forming is the process of deforming a sheet of metal along an axis by pressing it between clamps. A single sheet metal may be subject to a sequence of bends resulting in complex metal parts such as electrical lighting posts and metal cabinets.
These machines require very accurate control so as to ensure the required bending precision that is in the order of tens of microns. They have stringent safety requirements that also impose certain restriction on its operation. In addition to this, the production efficiency is also a very important factor in its operation.
In order to ensure production quality under these stringent requirements, it is important to make sure that all of the machines’ components are in perfect working order. The goal of this use case in the MANTIS project is to use a set of sensors to detect failures and then inform the maintenance staff of these events. In this work we used a top of the line Greenbender model to implement and test a system that could accomplish these goals.
A multi-disciplinary team participated in the research and development of this use case. The use case owner is the machine tool manufacturer ADIRA that sells machines worldwide. ADIRA’s main goal is to improve the maintenance services they provide to their customers.
Research and development in the area of communications was jointly done by ISEP and UNINOVA. This included the IoT architecture, sensors, communication’s hardware and infrastructure deployment. Data processing and analytics was performed by INESC and ISEP. INESC focused on root cause analysis (RCA), remaining useful life (RUL) forecasting and anomaly detection. ISEP worked on knowledge based techniques for failure detection by developing and testing a decision support system. In addition to this ISEP also developed a Human Machine Interface (HMI) application that provides access to IoT infrastructure and several MANTIS services, which includes the notification of failures.
JSI and XLAB also provided valuable input and feedback concerning the initial research and design tasks of the communications infrastructure (real time data transmission) and the HMI (usability).
The MANTIS project has provided INESC with the opportunity to research, test and apply machine learning techniques in a real-world setting. Tasks included the detailed study of the machine tools’ processes and components, eliciting requirements and information from the domain experts and evaluating several machine learning algorithms. Due to the many challenges that were faced in identifying, collecting and using sensor data, only anomaly detection is currently being deployed in this use case.
A set of 11 conditions are being continually monitored for anomalies. For each anomaly two thresholds are being used to identify respectively small and large deviations from the expected behavior. Whenever such a deviation is detected, an alert is dispatched to the HMI where the users are notified. These monitoring conditions should allow ADIRA to detect failures in the hydraulic system, numeric controller and several electric components. In addition to this, oil temperature and machine vibrations are also being monitored.
The MANTIS system, which includes INESC’s analytics module, has been deployed as a set of services in the Cloud. Initial tests show good false positive rates. We are now in the process of performing on-line evaluations of the detection rates. We are confident that these results will serve as an important firsts step for ADIRA to enhance its products by using more sophisticated and effective data analytics methods.
The Finnish use-case under the MANTIS project concentrates on proactive maintenance solutions in the field of conventional energy production. The industry is moving towards smaller distributed plants with less on-site staff and thus, the ability to deploy conventional CBM strategies has declined. However, availability is still a major factor in power generation efficiency and plant feasibility. Therefore, new kind of energy production asset maintenance solutions applicable also for less critical components are required.
Five industrial and academic partners, namely Fortum, Lapland University of Applied Sciences (LUAS), Nome, VTT and Wapice, form the Finnish consortium in the MANTIS project. The Finnish use-case of conventional energy production is centered on a flue gas blower in Fortum’s Järvenpää power plant. Power plants have a large array of rotating machinery, whose reliability greatly affect on the overall reliability of the plant. As such, the blower offers a valid testing environment for collaborative maintenance solutions developed by the Finnish partners. The blower has been instrumented with vibration sensors, virtual sensors and local data collectors provided by Nome, Wapice and VTT. The measurement data is stored in the MIMOSA data model based MANTIS database via REST interface developed by LUAS. The collected data can be distributed to individual systems across organizational boundaries for analysis purposes. The partners of the conventional energy production use-case have integrated their own analytic tools, such as Fortum’s TOPi, Nome’s NMAS and Wapice’s IoT-Ticket, to the MANTIS database, as illustrated in figure 1, and tested the system architecture successfully in practice.
The MANTIS project has offered a great opportunity for the conventional energy production use-case partners to develop their own HMIs that can be integrated to different fields of proactive maintenance. The development work continues in the third and last phase of the MANTIS project, as some advanced visualization approaches, including virtual reality and augmented reality applications, are piloted and integrated to the HMIs. The piloted cloud architecture from Fortum’s Järvenpää power plant will also be tested in larger scale in another entire power plant. The data collection will be extended to cover a wider range of equipment and process variables to enable plant-wide monitoring of assets and proactive maintenance strategies. In addition, the partners are developing their analytic tools further to provide solutions capable of diagnostics and prognostics required in advanced maintenance.
Liebherr participates in the MANTIS project as an industrial partner with the division Liebherr hydraulic excavators. As expected, the main expertise of Liebherr consists in developing and optimizing excavators under consideration of different information sources. However, after the delivery of the excavator to the customer, every excavator generates event respectively message data automatically, which are actually mainly used for fault diagnostics but not extensively for further investigations.
This event data logger records among other things basically:
timestamp, when an event occurs
type of event, e.g. info, warning or error
unique message identifier of this event class
In combination with anonymized data concerning service partner and customer the following questions are relevant:
Is there a relation between the message patterns and the corresponding anonymized service partner?
Is there a relation between the message patterns and the anonymized customer?
Analysis approach for clustering
The related analysis was performed by the University of Groningen (RUG) as a research partner within the MANTIS project by considering each excavator as a stochastic message generator. In the context of preprocessing, the different messages were first counted per excavator and afterwards normalized with the total amount of occurrence per unique message identifier.
Based on the computed message probabilities per machine a k-means clustering was performed. To overcome initialization influences the clustering was performed 100 times with random initialization. The relationship of the cluster assignment of each excavator with the corresponding service partner or customer for each ‘k’ was subsequently examined with the chi-square test. The average estimate of the significance of the 100 model estimations of each ‘k’ then represented the quality function.
Results of cluster analysis
As can be seen in figure 1, there is no tendency for a relationship between the service partner and the messages per excavator. The average significance level is obviously higher than 0.05 and all of the single levels have nearly the same magnitude.
In contrast to figure 1, figure 2 shows a clear minimum at k=7, indicating that for this number of groups, it is likely that the distribution of machines over customers is not likely to be random. Although the p_signif – value is with 0.0588 slightly above the significance level of 0.05, the magnitude at k=7 is obviously lower than at other k-values.
In order to explain the minimum at k = 7, Liebherr decoded the anonymized customers and tried to find manually a description of the clusters. The cumbersome work did actually not yield to the expected result, namely the detection of short cluster descriptions, but rather to the recognition of customer data mismatching.
In summary the carried out analysis pointed out, that with the skillful usage of analysis algorithms superficial unmanageable data can disclose insights. But one of the basic requirements for later usage of the results is the proper preparation of data.
Goizper S. Coop’s products are mechanical components (clutch brakes, gear boxes, indexing units…) installed within different kind of productive machines. These machines are designed to produce continuously and unplanned downtimes generate high costs. These components are the key part of some of the mentioned productive machines and the relevant component’s health influences directly within the machine status.
Breakdown of Components
Furthermore, if one of these components fails, it takes a long time while a new one is sent to the customer’s facilities, removed the old one and set up the new one. In these cases, the production asset maintenance means lot of expenses for customers and suppliers.
MANTIS for predictive maintenance
MANTIS platform provides an online and future view of these components’ health. Smart sensors installed at the mechanical component are connected to Monitoring and Alerting, which is performed automatically, within the smart-G box located next to the mechanical component. Then, this Big Data is processed in the Cloud and through different Maintenance data analytics the status and future trend of the component health is obtained as an output.
Obviously, the introduction of this Cyber Physical System will not eliminate all machine breakdowns, but it will help in order to reduce considerably machine unplanned downtimes, so that the customer and supplier will be able to plan their maintenance tasks and reduce these kind of stops.
Within the MANTIS ECSEL project, Goizper has collaborated close to one of its customers, Fagor Arrasate, trying to improve the real inconveniences and reduce expenses that unplanned downtimes cause in both firms.
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.