Department of Computer Science

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Browsing Department of Computer Science by browse.metadata.advisor "Herbst, Barend Mattheus"

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    A deep framework for predictive maintenance
    (2021-12-01) Steyl, Charl Cilliers; Hoffmann, McElory R.; Grobler, Trienko Lups; Herbst, Barend Mattheus
    ENGLISH ABSTRACT: Predictive maintenance (PdM) is a well-known maintenance approach that comprises of two problems, machine prognostic modelling and maintenance scheduling. The objective of prognostic modelling is to predict faults in machine components such as aircraft engines, lithium-ion batteries or bearings. The objective of maintenance scheduling is to reduce the cost of performing maintenance once the future degradation behaviour of a component has been established. Sensors are used to monitor the degradation behaviour of components as they change over time. Supervised learning is a suitable solution for prognostic modelling problems, especially with the increase in sensor readings being collected with Internet of Things (IoT) devices. Prognostic modelling can be formulated as remaining useful life (RUL)- or machine state estimation. The former is a regression- and the later is a classification problem. Long short-term memory (LSTM) recurrent neural networks (RNNs) are an extension of traditional RNNs that are effective at interpreting trends in the sensor readings and making longer term estimations. An LSTM uses a window of sequential sensor readings when making prognostic estimates which causes it to be less sensitive to local sensor variations, which results in improved prognostic model performance. In this study we create a framework to implement PdM approaches. The work consists of a codebase which can be used to create testable, comparable and repeatable prognostic modelling results and maintenance scheduling simulations. The codebase is designed to be extensible, to allow future researchers to standardise prognostic modelling results. The codebase is used to compare the prognostic modelling performance of an LSTM with tradition supervised prognostic modelling approaches such as Random Forests (RF)s, Gradient boosted (GB) trees and Support Vector Machines (SVM)s. The prognostic models are tested on three well-known prognostic datasets, the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) engine aircraft-, Center for Advanced Life Cycle Engineering (CALCE) battery- and Intelligent Maintenance Systems (IMS) bearing datasets. During the study we highlight factors that influence prognostic model performance, such as the effect of de-noising sensor readings and the size of the sample window used by the LSTM when making estimations. The results of the prognostic models are compared with previous studies and the LSTM shows improved performance on considered cases. The developed prognostic models are used to perform preventative maintenance scheduling with assumed costs in two simulations. The objective is first to compare the efficacy of traditional maintenance approaches, such as a mean time between failure (MTBF) strategy, with a PdM strategy, and second to investigate the effect of using a better performing prognostic model (such as the LSTM) in a PdM strategy. The improvements are measured by the reduction in costs. Key words: Predictive maintenance; remaining useful life; machine state estimation; preventative maintenance scheduling.