By Gang Niu
This e-book introduces condition-based upkeep (CBM)/data-driven prognostics and well-being administration (PHM) intimately, first explaining the PHM layout procedure from a structures engineering standpoint, then summarizing and elaborating at the data-driven technique for function development, in addition to feature-based fault analysis and analysis. The publication features a wealth of illustrations and tables to aid clarify the algorithms, in addition to useful examples exhibiting easy methods to use this instrument to unravel events for which analytic suggestions are poorly ideal. It equips readers to use the thoughts mentioned so as to learn and remedy various difficulties in PHM procedure layout, function building, fault analysis and prognosis.
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Extra resources for Data-Driven Technology for Engineering Systems Health Management: Design Approach, Feature Construction, Fault Diagnosis, Prognosis, Fusion and Decisions
The close relationships of these partners in the design and production process are reflected in the coupling of the information flow and analysis required to support veriﬁcation. At the completion of the design synthesis and integration stage, the SHM detailed design approach should be integrated with overall system design in a way that satisﬁes SHM requirements and is fully supported by interorganizational hardware design and analysis data. Throughout the functional analysis and design synthesis stages, trade studies are continually performed that evaluate the efﬁcacy of the proposed failure detection, isolation, prediction, and mitigation approaches with alternatives.
6 A Proposed PHM Design Approach for Rotary Machinery Systems To conduct step-by-step design and deployment of a PHM system, 5S approach is adopted to convert multivariate data to abstract prognostics information, utilizing different computing tools for different steps (Lee et al. 2013). 5S, as shown in Fig. 7, stands for Streamline, Smart Processing, Synchronize & See, Standardize, and Sustain. The ﬁrst “S,” Streamline, focuses on identifying critical components and prioritizing data to ensure the accuracy of the second “S,” which is Smart Processing.
Based on this signal, one makes decision about the operating condition of the machinery. The other techniques for fault detection are model-based and data-driven, as shown in Fig. 3. Model-based technique relies on an accurate dynamic model of the system and is capable for detecting even unanticipated faults. It takes advantage of the actual system and model outputs to generate a discrepancy or residual, as it is known, between two outputs that are indicative of a potential fault condition. In this method, bank of ﬁlter can be used to pinpoint the identity of the actual fault component.