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Diagnosis and condition monitoring in rotating machinery has been a subject of intense research for the last century. Recent developments indicate the drive towards integration of diagnosis and prognosis algorithms in future integrated vehicle health management (IVHM) systems. With this in mind, this paper concentrates on highlighting some of the latest research on common faults in rotating machines. Eight key faults have been described; the selected faults include unbalance, misalignment, rub/looseness, fluid-induced instability, bearing failure, shaft cracks, blade cracks, and shaft bow. Each of these faults has been detailed with regard to sensors, fault identification techniques, localization, prognosis, and modeling. The intent of the paper is to highlight the latest technologies pioneering the drive towards next-generation IVHM systems for rotating machinery.

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Excessive levels of unbalance in rotating machinery continue to contribute to machine downtime and unscheduled and costly maintenance actions. Whilst unbalance as a rotordynamic fault has been studied in great detail during the last century, the localization of unbalance within a complex rotating machine is today often performed in practice using little more than ‘rules of thumb’. In this work, localizing excessive unbalance has been studied from an experimental perspective through the use of two rotordynamic test rigs fitted with multiple disks. Sub-synchronous non-linear features in the frequency domain have been identified and studied as a method of aiding the localization of unbalance faults, particularly in situations where sensor placement options are limited. The results of the study are discussed from the perspective of next-generation Integrated Vehicle Health Management (IVHM) systems for rotating machines.

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