A Passive Diagnostic Experiment With Ergodic Properties

Condition based maintenance of machine tools—A review

“…To derive the explicit relationship between the condition variables and the lifetimes (current lifetime and failure lifetime) is a different mode of applying physical modelbased approaches to prognosis through mechanistic modeling for machines. It is considered as energy processors subject to vibration monitoring and for bearings with vibration monitoring [42,43]. Fig.…”

Machine Fault Diagnosis and Prognosis: The State of The Art

“…A general method was purposed by Chelidze and Cusumano [108] for tracking the evolution of hidden damage process in the situation that a slowly evolving damage process is coupled to a fast, directly observable dynamical system. Some different approaches used model-based techniques for prognosis were proposed in [109][110][111][112][113][114]. However, model-based techniques are merely applied for some specific components and each requires a different mathematical model.…”

“…Model-based approaches [104][105][106][107][108][109][110][111][112][113][114] • Can be highly accurate • Require less data then data-driven approaches…”

Application of Energy Processor Model for Diagnostic Symptom Limit Value Determination in Steam Turbines

“…We thus have a set of symptom life curves S G ( ) [assuming that symptom operator (S) is known] and, formally, X G is a random variable, so S G ( ) is a stochastic process. If this process is ergodic, as it is the case with most processes corresponding to stationary physical phenomena [4], we may change domains from time to symptom value S (see [5]) and derive symptom probability density histogram from the set of N symptom life curves. Now, for a given symptom operator , we may analytically derive a corresponding equation for symptom probability density R(S), de"ned [1] as the probability that this symptom will exceed a given value with the machine remaining able for normal operation:…”