In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called 'health factors' or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.

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In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.

Predictive Maintenance methods are aimed to obtain reliable estimates of the remaining life cycle of an equipment from time series of suitable process parameters, named "health factors", typically exhibiting a monotone evolution associated with the equipment wear. The present study was motivated by the predictive maintenance of a dry etching equipment within a semiconductor manufacturing process. The optimal prediction of the health factor, represented by the cooling helium flow, must cope with noisy measurements of the health factor (possibly masking its monotonicity) and non uniform sampling times. The problem is formulated as a stochastic filtering problem in which a stochastic process has to be optimally predicted based on noisy and irregularly sampled observations. In particular, a hidden Gamma process model is proposed in order to capture all the features of the health factor, namely its nonnegativity and nonnegativity of its derivative. Since this filtering problem is not amenable to a closed form solution, a numerical Monte Carlo approach based on particle filtering is developed. Additionally, an adaptive parameter identification procedure is proposed to achieve the best trade off between promptness and noise insensitivity.