Model Based Monitoring of Dynamic Loads and Remaining Useful Life Prediction in Rolling Mills and Heavy Machinery
Operation of industrial metallurgical plants is associated with significant wear in spindles, gearboxes and bearings where difficult to implement digital diagnostic tools due to harsh operating conditions. Angular and radial gaps produce extremely high dy
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Model Based Monitoring of Dynamic Loads and Remaining Useful Life Prediction in Rolling Mills and Heavy Machinery Pavlo Krot , Ihor Prykhodko , Valentin Raznosilin , and Radoslaw Zimroz
Abstract Operation of industrial metallurgical plants is associated with significant wear in spindles, gearboxes and bearings where difficult to implement digital diagnostic tools due to harsh operating conditions. Angular and radial gaps produce extremely high dynamic loads and abrupt failures in the multi-stand hot rolling mills. Reliable vibration monitoring is very complicated due to inherent changes of technological regimes, treated material and drive speed. It appears more beneficial to monitor dynamic torques in addition to vibration signals, but this is restricted to the installation of strain gauges. The more acceptable approach is to monitor static torques of electric motors and, having identified multi-body models, to calculate remaining useful life (RUL) of elements. Based on this approach, the new monitoring system is developed for the multi-stand mill with integration into plant automation infrastructure. Parameters adaptation of nonlinear dynamical models is provided and technological loads optimization by the criterion of RUL in rolling stands. System supports a database of maintenance actions and elements failures. Reports are generated on overloading and RUL.
P. Krot (&) R. Zimroz Department of Machine Systems, Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Wroclaw, Poland e-mail: [email protected] R. Zimroz e-mail: [email protected] I. Prykhodko V. Raznosilin Department of Metal Forming Processes and Machines, Iron and Steel, Institute of Z.I. Nekrasov, National Academy of Sciences of Ukraine, Dnipro, Ukraine e-mail: [email protected] V. Raznosilin e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_34
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Introduction
Currently, steel manufacturers are facing a problem to increase the reliability of existing heavy mills to produce wide (up to 5000 mm) and thin (up to 0,8 mm) hot rolled strips of high-grade steels required for different new applications. The main aggregates of rolling mills drivelines were not initially designed for such products and in addition, has deterioration. The increase of reliability can be achieved by passive damping of excessive dynamic loads [1–3], active torsional vibration control [4–7], optimization of technological regimes [8–10] and mill units tuning [11]. Cryogenic treatment is an efficient method to increase wear resistance [12]. Meanwhile, severe failures occur [13–15] in many parts of hot rolling mills (see Fig. 34.1). Investments in the new rolling mills are a rare case and maintenance staff needs computerized tools to predict abrupt failures and to plan repairs. Global digitalization of ste
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