Hybrid Models for Rotating Machinery Diagnosis and Prognosis : Estimation of Remaining Useful Life
Year: 2014
Type: report
Abstract: The purpose of this literature review is to summarise the various technologies that can be used for machinery diagnosis and prognosis. The review focuses on Condition Based Maintenance (CBM) in machinery systems, with a short description of the theory behind each technology; it also includes references to state-of-the-art research into each theory. When we compare technologies, especially with respect to cost, complexity, and robustness, we find varied abilities across technologies. The machinery health assessment for CBM deployment is accepted worldwide; it is very popular in industries using rotating machines involved. These techniques are relevant in environments where predicting a failure and preventing or mitigating its consequences will increase both profit and safety. Prognosis is the most critical part of this process and is now recognised as a key feature in maintenance strategies; the estimation of Remaining Useful Life (RUL) is essential when a failure is identified. The literature review identifies three basic ways to model the fault development process: with symbols, data, or mathematical formulations based on physical principles. The review discusses hybrid approaches to machinery diagnosis and prognosis; it notes some typical approaches and discusses their advantages and disadvantages.
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2189521609