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.
The researchers team consist of:
- ISEP (Instituto Superior de Engenharia do Porto)
- UNINOVA (Instituto de Desenvolvimento das Novas Tecnologias)
- INESCTEC (Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência)
- JSI (Jožef Stefan Institute)
- XLAB (XLab Razvoj Programske Opreme In Svetovanje)
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.
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.