Dragan Banjevic

Abstract: Abstract The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models that attempt to forecast machinery health based on condition data. This paper presents a novel approach for incorporating population characteristics information and suspended condition trending data of historical units into prognosis. The population characteristics information extracted from statistical failure distribution enables longer-range prognosis. The accurate modelling of suspended data is also found to be of great importance, since in practice machines are rarely allowed to run to failure and hence data are commonly suspended. The proposed model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function (PDF) estimator. The trained network is capable of estimating the future survival probabilities of an operating asset when a series of condition indices are inputted. The output survival probabilities collectively form an estimated survival curve. Pump vibration data were used for model validation. The proposed model was compared with two similar models that neglect suspended data, as well as with a conventional time series prediction model. The results support our hypothesis that the proposed model can predict more accurately and further ahead than similar methods that do not include population characteristics and/or suspended data in prognosis.

TL;DR: This paper proposes a model to find the optimal periodic inspection interval on a finite time horizon for a complex repairable system where soft failures are detected and fixed only at planned inspections, but not at moments of hard failures.

TL;DR: The paper reports the development of an optimal maintenance program based on vibration monitoring of critical bearings on machinery in the food processing industry and concludes that it is possible to identify key measurements for examination at the time of vibration monitoring – thus possibly saving on inspection costs.

TL;DR: In this article, a fleet of 55 haul truck wheel motors were analyzed along with their respective failures and repairs over a nine-year period and a decision model that provided an unambiguous and optimal recommendation on whether to continue operating a wheel motor or to remove it for overhaul on the basis of data obtained from an oil sample.

TL;DR: In this paper, the fault growth parameter (FGP) from the residual error signal was calculated using the proportional-hazards modelling technique and several statistical and replacement decision models were built based upon the observed condition data and ensuing failure events.