Machine integrated health models for condition-based maintenance

Strojevi su podložni degradaciji zbog tehničkih kao i ne-tehničkih faktora koji im povećavaju mogućnost kvarova i pogoršavaju njihovo stanje zdravlja. Zbog toga raste zanimanje za nove metode procjenjivanja zdravstvenog stanja. Sada se inherentno zdravlje stroja procjenjuje praćenjem podataka koje pružaju senzori. Drugim riječima, razvoj inherentnog zdravlja ovisi jedino o razvoju tehničkih čimbenika te stoga ne daje sveobuhvatnu informaciju o stanju stroja. Ovaj rad uvodi koncepte "inherentnog zdravlja" i "integriranog zdravlja" kao i njihovu povezanost. Na osnovu procjene inherentnog zdravlja, integrirano zdravlje uzima u obzir ne-tehničke faktore koji se odnose na starost, radne uvjete i održavanje stroja. Učinkovitost održavanja se također razmatra integrirajući sekvencijalnu nesavršenu politiku održavanja u strategiju održavanja koja se zasniva na integriranim zdravstvenim uvjetima. Sveobuhvatnom procjenom i otkrivanjem u stvarnom vremenu stanja integriranog zdravlja, ovaj se model može koristiti kao podrška upravljanju zdravljem stroja i donošenju odluke o održavanju. U analizi pojedinih slučajeva, očekuje se da će se očite razlike između inherentnog zdravlja i integriranog zdravlja pojaviti u određenim uvjetima.Machines undergo degradation as a result of both technical factors and non-technical factors that increase the potential for failures and deteriorate their health condition, and there is growing interest in new methods for health condition assessment. Currently, the inherent health of a machine is evaluated by monitoring of the data acquired by sensors. In other words, the evolution of the inherent health depends only on the evolution of technical factors, and therefore does not comprehensively represent the overall condition of the machine. This study introduces the concepts of "inherent health" and "integrated health" as well as their relationship. On the basis of inherent health assessment, the integrated health considers the non-technical factors related to the age, working conditions, and maintenance of a machine. By integrating a sequential imperfect maintenance policy into the maintenance strategy based on the integrated health conditions, the maintenance effectiveness is also considered. Through comprehensive assessment and real-time detection of the integrated health condition, this model may be used to support machine health management and maintenance decision-making. In case studies, the obvious differences between inherent health and integrated health are expected to appear under certain circumstances

Condition based maintenance using proportional hazards model

Condition-based maintenance (CBM) is an advanced maintenance strategy in which maintenance actions are scheduled based on both the age data and condition monitoring information. Proportional Hazards Model (PHM) is a powerful statistical tool for estimating the equipment failure rate under condition monitoring. Effective CBM using PHM can decrease the overall maintenance costs by reducing unnecessary scheduled preventive maintenance actions. In CBM using PHM, main optimization objectives including minimizing maintenance costs and maximizing equipment reliability typically conflict to each other. But the reported research only focuses on single-objective. In this thesis, we propose a multiple-objective CBM optimization approach based on physical programming, which can systematically balance the tradeoff between the optimization objectives and find the optimal solution that best represents the decision maker's preference on the objectives. In CBM using PHM, the accuracy of parameter estimation greatly affects the accuracy of the model in representing and predicting the equipment health condition. Traditional optimization methods such as Newton's methods are inaccurate because they can only find local optimal value in parameter estimation. In this thesis, we develop an approach based on Genetic Algorithms (GA) for PHM parameter estimation and this approach can improve the accuracy of parameter estimation significantly. To illustrate the proposed approaches, we conduct two case studies using real-world vibration monitoring data, shearing pump bearings in a food processing plant and Gould pump bearings at Canadian Kraft Mill. The proposed approaches contribute to the general knowledge of condition based maintenance, and have the potential to greatly benefit various industries