Neil Montgomery

TL;DR: In this paper, a model consisting 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 is presented.

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: Although younger women have a slower transition rate from healthy state to preclinical, their screen-detected tumour becomes evident sooner and women aged 50–59 have a higher mortality rate compared with younger women.

TL;DR: The impact of minor maintenance on CBM models is discussed and a dataset on a collection of gearboxes, consisting of reliability and oil analysis information, including data on oil changes and oil additions, is used to illustrate the benefit of modelling minor maintenance actions.

TL;DR: A multi-state Markov model for breast cancer progression, considering both the screening and treatment stages of breast cancer, reveals that cost per life saved increases with an increase in screening frequency, and it is found that screening annually for all age groups is associated with the highest costs per lives saved.