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- Cyber-physical production systems supported by intelligent devices (smartboxes) for industrial processes digitalizationPublication . Torres, Pedro; Dionísio, Rogério; Malhão, Sérgio; Neto, Luis; Ferreira, Ricardo; Gouveia, Helena; Castro, Helderndustry 4.0 paradigm is a reality in the digitization of industrial processes and physical assets, as well as their integration into digital ecosystems with several suppliers of the value chain. In particular, Industry 4.0 is the technological evolution of embedded systems applied to Cyber-Physical Systems (CPSs). With this, a shift from the current paradigm of centralization to a more decentralized production, supported by Industrial Internet of Things (IIoT), is implied. The work reported in this paper focuses on the development of smart devices (SmartBoxes), based on low-cost hardware such as Raspberry Pi and also platforms certified for industrial applications, such as NI CompactRIO. Both platforms adopted the OPC-UA architecture to collect data from the shop-floor and convert it into OPC-UA Data Access standard for further integration in the proposed CPPS. Tests were also performed with the MQTT protocol for monitorization. Each SmartBox is capable of real-time applications that run on OPC-UA and MQTT, allowing easy interaction between supervisory systems and physical assets.
- Magnetoresistive sensors and piezoresistive accelerometers for vibration measurements: a comparative studyPublication . Dionísio, Rogério Pais; Torres, Pedro; Ramalho, Armando; Ferreira, Ricardohis experimental study focuses on the comparison between two different sensors for vibration signals: a magnetoresistive sensor and an accelerometer as a calibrated reference. The vibrations are collected from a variable speed inductor motor setup, coupled to a ball bearing load with adjustable misalignments. To evaluate the performance of the magnetoresistive sensor against the accelerometer, several vibration measurements are performed in three different axes: axial, horizontal and vertical. Vibration velocity measurements from both sensors were collected and analyzed based on spectral decomposition of the signals. The high cross-correlation coefficient between spectrum vibration signatures in all experimental measurements shows good agreement between the proposed magnetoresistive sensor and the reference accelerometer performances. The results demonstrate the potential of this type of innovative and non-contact approach to vibration data collection and a prospective use of magnetoresistive sensors for predictive maintenance models for inductive motors in Industry 4.0 applications.