Chair: Prof. Qingkai HAN (Dalian University of Technology)
Using physical based models for machinery RUL estimation Prof. JACOB BORTMAN (BGU)
Machinery prognosis is the process of forecasting the remaining operational life (RUL), future condition, or probability of failure based on the acquired condition monitoring data. RUL forecast becomes crucial since it allows planning of efficient maintenance actions.
In order to estimate the remaining usefull life it is needed, first, to estimate the damage severity or size, and then, to forecast the damage growth model.
Damage size estimation is not yet developed and needs additional research. Classic theories assume that energy dissipation is proportional to fault size. For small faults, it is not correct. In general the energy-fault size relation depends on the transmission path from the defect to the snsor which differs for every case of defect location, machine and sensor. Therefore the estimation of the fault size is crucial. State of the art algorithm for bearings size estimation will be presented. The algorithm approach is based on insights from a general dynamic bearing model and an analytical model for RE-spall interaction. The outline of these models will be presented.
Other machine components such as, gears and shafts will be discussed.
In the second stage, understanding of the damage progression process is needed. This stage is essential for estimation the RUL. The required steps towards damage progression tools will be discussed.
The models allow to examine the the algorithms sensitivity to different types of faults, geometric errors, misalignment, unbalance, system parameters etc.
Towards a Physics Based Foundation for the Estimation of Bearings RUL Prof. Jacob BORTMAN, Dmitri GAZIZULIN and Renata KLEIN (Ben-Gurion University of the Negev, R.K. Diagnostics)
Rolling element bearing (REB) prognosis is the process of forecasting the remaining operational life, future condition, or probability of failure based on the acquired condition monitoring data. One of the common reasons for rolling element bearings failure is the rolling contact fatigue (RCF). Complete understanding of the fatigue process is critical for estimation of the bearing remaining useful life (RUL) and allows planning maintenance actions. In the current work, it is assumed that the spall generation, on the surface of the raceway, is a result of RCF. However, after the first spall formation, the bearing might be fully operational for millions of cycles. Thus, for the estimation of the bearing RUL it is also important to understand the damage propagation process. The proposed method of RUL prediction is separated into two steps: diagnostics and prognostics. The diagnostics includes characterization of the defect in terms of location, type, and extent. The prognostics includes estimation of the defect propagation as a function of time, using its characterization derived from the diagnostics step. It is expected that results of the current study will provide an estimation of the bearing’s RUL: from first spall formation to the unoperational bearing. The spall generation process, as a result of RCF, is modeled based on continuum damage mechanics with representation of material grain structure and implemented using a Finite Element software. The results of the model are in a good agreement with published theoretical and experimental data. The paper also includes a discussion on the ongoing research and the methodology that will be implemented as part of it.
A Practical Guide for the Characterization of Lithium-Ion Battery Internal Impedances in PHM Algorithms Mr. Aramis PEREZ, Matias BENAVIDES, Heraldo ROZAS, Sebastian SERIA and Marcos ORCHARD (University of Chile)
This article aims at describing the most important aspects to be considered when using the concept of battery internal impedances in algorithms that focus on characterizing the State-of-Health (SOH) degradation of lithium-ion (Li-ion) batteries. The first part provides a brief literature review that will help the reader to interpret the outcome of typical Li ion discharge and/or degradation tests. The second part of the paper shows preliminary results for accelerated degradation experiments performed on a Li-ion cell under controlled conditions. Results show changes on electrochemical impedance spectroscopy test that can be linked to battery degradation. This knowledge may be of great value when implementing algorithms aimed at predicting the battery End-of-Life (EoL) in terms of temperature, voltage, and discharge current measurements.
Data-Driven Prognostics for Major Piping in Nuclear Power Plants Mr. Gibeom KIM, Hyeonmin KIM, Yoon-Suk CHANG, Seungho JUNG and Gyunyoung HEO (Kyung Hee University, Korea Atomic Energy Research Institute, Ajou University)
As operation period of Nuclear Power Plants (NPPs) is getting longer, necessity of reflecting ageing effect is increasing. Especially, when it comes to the piping in NPPs such as reactor coolant system piping or steam generator tubes, it is vulnerable to stress corrosion crack (SCC) or wear due to the fluid with high temperature, high pressure and radiation. Accidents related to such cases have been reported. Since ruptures of the piping can result in severe accidents, it is important to predict and prevent them in advance. Current NPPs ageing management is performed with the physical model based on generic experimental data, which cannot properly consider each NPPs’ different operation environment or history. Prognostics using plant specific data can compensate this limit of ageing management using the physical model. Recently, as usable data of NPPs is increasing with the development of instrumentation technology, applicability of prognostics for NPPs has been increased. Therefore, this paper suggests some prognostics methods such as GPM, MCMC and Particle filter that can consider ageing degradation for the major piping in NPPs. Because prognostics is performed with each NPPs’ own data, it can consider each NPPs different condition. Thus, reflecting prognostics results to Probabilistic Safety Assessment (PSA) that is used for evaluating safety of NPPs will make it possible to perform quantitative safety assessment considering current or future ageing degradation.