From the Internet of Things to the Industrial Internet of Things
After the Internet of Things, the Industrial Internet of Things or Industry 4.0. As we can experience every day, the simple fact of having connectivity immediately introduces clear improvements to our lives. So why do not apply the same logic to industrial processes, such as monitoring or remote service? With a number of IIoT devices destined to grow exponentially (to reach 5 billion by 2020), machine and component manufacturers are facing a new challenge and a great opportunity: the functional use of the data collected, through their analysis and modeling.
Use of detected data and predictive maintenance
In just a few years, thanks to the IIoT devices, the gap between IT and Operation Technology has been bridged and the rate at which digital technologies are providing data from the machine itself has reached exceptional levels. It’s difficult to predict what the potential fields of application for this new immense resource will be, but the first concrete effect on which we want to dwell is in the quality of maintenance.
Elements of the IIoT and mathematical analysis of “big data” are in fact increasingly used to predict the risk of interruptions due to malfunctions, allowing timely and targeted maintenance interventions, optimizing resources: thanks to the development of behavior of the machines on the basis of the analyzed data, it’s now possible to identify the real residual time before breakdown and to know in advance which element could break. All this is defined with the term “predictive maintenance”.
Big-data and collateral data to work out a predictive model
The physic dimensions involved in the predictive process can be used at SCADA level and easily populate the “big data”, because they are already monitored during the ordinary control of the process under examination. Other dimensions are detectable with a virtual sensor technique, recording from SW the number of actions carried out by the actuators, their operating hours or the times of some processes, without having to set up the machine with additional sensors. For a complete and effective predictive ability, the algorithms must monitor the compliance with the conditions of acceptability in which the system operates and correlate direct and functional data to the process, from the devices already involved, to the thermal conditions that are not always foreseeable in the design step.
Moreover, the different situations of air pollution for filtration devices and for heat exchangers imply a degradation of efficiency that can not be easily planned, because not necessarily constant or be conditioned by exceptional and fortuitous events. In the same way, the efficiency of the air conditioners declines with during operation, but also according to the different loading conditions. In other words, if the thermal design of the electrical cabinet on board the machine is vital for the robustness of the whole system, the certainty of its total effectiveness during the life of the system, is not less.
Climatic conditions and predictive maintenance with IIoT
For the purposes of maintenance, the absence of data on the thermal conditions of the electrical panel on board can therefore represent a considerable gap, but what we can do? The thermal regulation is, by tradition, entrusted with stand-alone controls, almost always electromechanical or controlled by air conditioners that have remote access only on the most advanced models. The apparently simpler way is to expand the I/O channels of the PLC already present in the machine, equip the sensor system dedicated to the monitoring of fans, cooling units, anti-condensation heaters and integrate the thermal management and remote assistance between the activities carried out by the PLC itself or other equivalent devices. However, this increases the complexity of the system, with the consequent weighting of the process control cycle and increases in wiring costs: if this path is therefore not advisable for new plants, for a possible revamping of a traditional system to convert to Industry 4.0, is completely impossible. Accessory data collection systems based on “wireless” sensors and related concentrators make possible to simplify wiring, but end up burdening costs. We therefore need new generation thermal management controls, which can be integrated into the factory network, compact, able to control and monitor temperatures, humidity, efficiency of air conditioning and ventilation systems, at acceptable costs and prepared both for installation on new plants and for the renovation of existing cabinets.
The future of thermal management of electrical panels will be Sensis by Fandis
Sensis, the new product developed by Fandis, is based on these elements. In the space of only one electromechanical thermostat on the DIN bar (measuring only 35 x 98 x 118 mm), Fandis concentrated in Sensis the response to all thermal management requirements, according to the Industry 4.0 and IIoT paradigm, with an authentic eye open on predictive maintenance .
Sensis measures the temperature and humidity in different places of the enclosure, combining the values, and constantly monitors the ventilation systems. At the same time, Sensis identifies the peak values or the exceeding of critical thresholds or the opening of the cabinet door, it records the time, date and duration over time allowing to trace and combine all information for diagnostic purposes. It has a wide range of models for total compatibility with the main field buses and can bypass the PLC by transmitting via OPC-UA or MQTT on the same physical channel.
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