By Gang Niu
This booklet introduces condition-based upkeep (CBM)/data-driven prognostics and health and wellbeing administration (PHM) intimately, first explaining the PHM layout strategy from a platforms engineering standpoint, then summarizing and elaborating at the data-driven technique for characteristic building, in addition to feature-based fault analysis and analysis. The publication features a wealth of illustrations and tables to assist clarify the algorithms, in addition to functional examples exhibiting tips to use this device to resolve events for which analytic strategies are poorly perfect. It equips readers to use the recommendations mentioned on the way to learn and remedy numerous difficulties in PHM process layout, characteristic development, fault analysis and prognosis.
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Additional info for Data-Driven Technology for Engineering Systems Health Management: Design Approach, Feature Construction, Fault Diagnosis, Prognosis, Fusion and Decisions
The main disadvantage is that data-driven approaches may have wider conﬁdence intervals than other approaches and that they require a substantial amount of data for training. Data-driven approaches can be further subcategorized into fleet-based statistics and sensor-based conditioning. In addition, data-driven techniques also subsume cycle counting techniques that may include domain knowledge. As mentioned, a principal bottleneck is the difﬁculty in obtaining run-to-failure data, in particular for new systems, since running systems to failure can be a lengthy and rather costly process.
2003). Veriﬁcation of the capability of these maturing technologies (providing product differentiation or other advanced capabilities to support system design goals) to support the requirements for which they are intended is performed in this stage. Partner design instantiations are veriﬁed by analysis and benchmarking against the 26 2 Design Approach for Systems Health Management agreed-to system performance metrics, and interfaces are evaluated for compatibility. This stage initiates the process of veriﬁcation and validation (V&V) of the SHM system.
There is a great deal of uncertainty associated with the design, development, and deployment of a HM system, and often, its performance cannot be accurately predicted due to emergent “metasystem” behaviors deriving from the interaction of the engineered system and its operating environment (as discussed early in this chapter). The more the system is exercised, and particularly if it can be exposed to operational conditions that may exceed the envelope considered during initial design, the better the chance one has to observe and then correct the root cause of the issue prior to actual deployment.
Data-Driven Technology for Engineering Systems Health Management: Design Approach, Feature Construction, Fault Diagnosis, Prognosis, Fusion and Decisions by Gang Niu