Proactive Condition Monitoring Of Low Speed Machines
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Proactive Condition Monitoring of Low-Speed Machines
This book broadens readers’ understanding of proactive condition monitoring of low- speed machines in heavy industries. It focuses on why low-speed machines are different than others and how maintenance of these machines should be implemented with particular attention. The authors explain the best available monitoring techniques for various equipment and the principle of how to get proactive information from each technique. They further put forward possible strategies for application of FEM for detection of faults and technical assessment of machinery. Implementation phases are described and industrial case studies of proactive condition monitoring are included. Proactive Condition Monitoring of Low-Speed Machines is an essential resource for engineers and technical managers across a range of industries as well as design engineers working in industrial product development.
Design of an Intelligent Embedded System for Condition Monitoring of an Industrial Robot
This thesis introduces a successfully designed and commissioned intelligent health monitoring system, specifically for use on any industrial robot, which is able to predict the onset of faults in the joints of the geared transmissions. However the developed embedded wireless condition monitoring system leads itself very well for applications on any power transmission equipment in which the loads and speeds are not constant, and access is restricted. As such this provides significant scope for future development. Three significant achievements are presented in this thesis. First, the development of a condition monitoring algorithm based on vibration analysis of an industrial robot for fault detection and diagnosis. The combined use of a statistical control chart with time-domain signal analysis for detecting a fault via an arm-mounted wireless processor system represents the first stage of fault detection. Second, the design and development of a sophisticated embedded microprocessor base station for online implementation of the intelligent condition monitoring algorithm, and third, the implementation of a discrete wavelet transform, using an artificial neural network, with statistical feature extraction for robot fault diagnosis in which the vibration signals are first decomposed into eight levels of wavelet coefficients.
Surface Mining Machines
This unique volume imparts practical information on the operation, maintenance, and modernization of heavy performance machines such as lignite mine machines, bucket wheel excavators, and spreaders. Problems of large scale machines (mega machines) are highly specific and not well recognized in the common mechanical engineering environment. Prof. Rusiński and his co-authors identify solutions that increase the durability of these machines as well as discuss methods of failure analysis and technical condition assessment procedures. "Surface Mining Machines: Problems in Maintenance and Modernization" stands as a much-needed guidebook for engineers facing the particular challenges of heavy performance machines and offers a distinct and interesting demonstration of scale-up issues for researchers and scientists from across the fields of machine design and mechanical engineering.