Vibration and temperature sensor network solutions: Case study for industry 4.0

In this paper a new predictive maintenance procedure for the industrial ventilation installation and industrial testing equipment is proposed. The idea started with the digital processing of signals provided from the temperature and vibration sensors located in the investigated installation. The sensor responses give us coherent and traceable information about the operating regime and the technical condition of the entire installation. In the first part tests on the ventilation devices were conducted in the laboratory, one with defective bearings and the other in good condition. Both are powered by three-phase electric motors. Additionally, tests were also conducted on an industrial gearbox testing equipment. For this a Raspberry Pi development board with an HMI (Human machine interface) and an open source platform for data monitoring were used. The sensors used were one temperature and one vibration displacement sensor. After processing the data the faulty bearings were easily observable, which is a real help in the predictive maintenance of the equipment because servicing ventilation devices require trained workers and large costs (because of exposure to risks). The data can be viewed in real time by authorised personnel who can log on to the cloud platform. Judging by the fact that most of the building stones used are open source projects, the costs of the above mentioned solution are low.