RESEARCH ON THE TECHNICAL CONDITION OF THE DRIVE BEARINGS OF THE QUAY CONTAINER HANDLER LIFTING MECHANISM

Abstract

Under modern conditions, most cargo handling operations related to container transportation are carried out using quay container handlers. Ensuring their uninterrupted and efficient operation requires maintaining a high level of technical reliability of the main equipment. Increased requirements for the operational reliability of handling mechanisms in port complexes necessitate the development and implementation of new, more advanced methods of technical diagnostics during their operation. Assessing the actual technical condition of a container handler solely by theoretical methods is practically impossible due to constantly changing loads and the dynamic operating modes of the equipment. Therefore, improving the operational reliability of container handlers is achieved through the development of effective methods for monitoring and diagnosing the condition of individual components of their structure. A priority task in this area is the timely determination of the technical condition of the lifting mechanism, since the presence of hidden defects in its components can lead to emergency situations, equipment failures, and significant production downtime. In this context, diagnostics allow the detection of mechanism faults without the need for dismantling or disassembly, which significantly reduces maintenance time. Currently, a number of methods for monitoring the technical condition of bearing assemblies in rotary mechanisms have been developed [1–7]. However, despite significant achievements in this field, these methods are often characterized by high implementation complexity, limited scope of application, and certain technical and operational restrictions. Therefore, improving the diagnostic system for the bearing assemblies of container handlers remains a relevant scientific and technical challenge. Vibration diagnostic methods used in modern studies enable comprehensive statistical analysis based on periodic and quasi-periodic random processes. This approach involves isolating modulating stationary components and performing subsequent spectral-correlation analysis of their parameters, which provides information about the nature and degree of defect development. Diagnostic features are formed using first- and second-order probabilistic characteristics; in particular, the analysis of second-order parameters allows the detection of defects at the early stages of their occurrence [2–6].