No abstract

The remote prognosis and diagnosis of bearings can prevent industrial system failures, but the availability of realistic experimental data, being as close as possible to those detected in industrial applications, is essential to validate the monitoring algorithms. In this paper, an innovative bearing test rig architecture is presented, based on the novel concept of “self-contained box”. The monitoring activity is applicable to a set of four middle-sized bearings simultaneously, while undergoing the independent application of radial and axial loads in order to simulate the behavior of the real industrial machinery. The impact of actions on the platform and supports is mitigated by the so-called “self-contained box” layout, leading to self-balancing of actions within the rotor system. Moreover, the high modularity of this innovative layout allows installing various sized bearings, just changing mechanical adapters. This leads to a reduction of cost as well as of system down-time required to change bearings. The test rig is equipped with suitable instrumentation to develop effective procedures and tools for in- and out-monitoring of the system. An initial characterization of the healthy system is presented.

The steel rolling process is critical for safety and maintenance because of loading and thermal operating conditions. Machinery condition monitoring (MCM) increases the system’s safety, preventing the risk of fire, failure, and rupture. Equipping the mill bearings with sensors allows monitoring of the system in service and controls the heating of mill components. Fiber optic sensors detect loading condition, vibration, and irregular heating. In several systems, access to machinery is rather limited. Therefore, this paper preliminarily investigates how fiber optics can be effectively embedded within the mill cage to set up a smart manufacturing system. The fiber Bragg gratings (FBG) technology allows embedding sensors inside the pins of backup bearings and performing some prognosis and diagnosis activities. The study starts from the rolling mill layout and defines its accessibility, considering some real industrial cases. Testing of an FBG sensor prototype checks thermal monitoring capability inside a closed cavity, obtained on the surface of either the fixed pin of the backup bearing or the stator surrounding the outer ring. Results encourage the development of the whole prototype of the MCM system to be tested on a real mill cage in full operation.

The minimization of friction losses in internal combustion engines is a goal of primary importance for the automotive industry, both to improve performance and to comply with increasingly stringent legislative requirements. It is therefore necessary to provide designers with tools for the effective estimation of friction losses from the earliest stages of design. We present a code for the estimation of friction losses in piston assembly that uses semianalytical models that require only strictly necessary geometric and functional inputs for the representation of components. This feature renders the code particularly suitable for the preliminary design phase. Furthermore, models ensure reduced computation times while maintaining excellent predictive capabilities, as demonstrated by the numerical–experimental comparison.

Maintenance scheduling is a fundamental element in industry, where excessive downtime can lead to considerable economic losses. Active monitoring systems of various components are ever more used, and rolling bearings can be identified as one of the primary causes of failure on production lines. Vibration signals extracted from bearings are affected by noise, which can make their nature unclear and the extraction and classification of features difficult. In recent years, the use of the discrete wavelet transform for denoising has been increasing, but studies in the literature that optimise all the parameters used in this process are lacking. In the current article, the authors present an algorithm to optimise the parameters required for denoising based on the discrete wavelet transform and thresholding. One-hundred sixty different configurations of the mother wavelet, threshold evaluation method, and threshold function are compared on the Case Western Reserve University database to obtain the best combination for bearing damage identification with an iterative method and are evaluated with tradeoff and kurtosis. The analysis results show that the best combination of parameters for denoising is dmey, rigrSURE, and the hard threshold. The signals were then distributed in a 2D plane for classification through an algorithm based on principal component analysis, which uses a preselection of features extracted in the time domain.