Diagnostic enhancements for air vehicle HUMS to increase prognostic system effectiveness

A review of diagnostic and prognostic capabilities and best practices for manufacturing

“…The ability to diagnose component faults in their infancy is currently limited, due in part to sufficiently large sensitivity to signal noise, dependence on environmental and operating conditions, lack of fault detection (Patrick et al 2009), and uncertainties in maintenance schedules. Consequently, most manufacturing operations are reactive so maintenance diagnosis is mainly a specialized process, as in the automotive manufacturing industry (Barajas and Srinivasa 2008).…”

“…Diagnostics are vital for successful prognostics because an acceptable prognostic method starts with robust diagnostics, since the uncertainties of the estimated system condition affect any future prediction (Hess et al 2005; Patrick et al 2009). Diagnostic challenges exist due to problems with verification and validation.…”

“…Prognostics is even more challenging than diagnostics, which is one main reason why prognosis is an underdeveloped element of PHM systems (Patrick et al 2009). Some failures are intermittent and hence difficult to predict (Sun et al 2010).…”

“…Hence, prognosis is the “recognized Achilles’ heel” of PHM (Ly et al 2009). In fact, few PHM methods produce continuous real-time estimation of the RUL (Patrick et al 2009) and improved methodologies are needed for RUL prediction based on physical and other measurements (United States Department of Defense 2008). For manufacturing and other systems, being complex systems with perhaps thousands of subsystems and various operational conditions, predicting the reliability and performance is even more difficult (Lee et al 2011).…”

“…The enabling technologies are based on health-monitoring software, data-processing methods for feature extraction, diagnostic and prognostic algorithms based on Bayesian estimation theory [specifically particle filtering (Orchard and Vachtsevanos 2007)], fatigue or degradation modeling, and real-time measurements (Ly et al 2009). As soon as a fault is detected, the PDF for that time is used as an initial condition for the prognostic routines utilizing a nonlinear dynamic state-space model (Patrick et al 2009).…”