Department of Computer Science

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https://scholar.sun.ac.za/handle/10019.1/96336

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Browsing Department of Computer Science by browse.metadata.advisor "Grobler, Trienko Lups"

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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.
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    Ergo: a gesture-based computer interaction device
    (Stellenbosch : Stellenbosch University, 2024-03) Kane, Boyd Robert; Grobler, Trienko Lups; Stellenbosch University. Faculty of Science. Dept. of Computer Science.
    ENGLISH ABSTRACT: This thesis presents Ergo, a bespoke glove‐based sensor suite designed to fully replace the regular QWERTY keyboard in terms of both number of input keys and speed of input. Ergo collects acceleration data from each of the user’s 10 fingertips at 40 times per second and is able to predict which of 50 differ‐ ent hand gestures is being performed at 40 times per second. The user does not need to explicitly mark the start or end of each gesture, as Ergo is able to automatically distinguish between intentional gestures made by the user and other non‐gesture movements. When a known gesture is detected, a corre‐ sponding keystroke is emitted, allowing the user to “type” on their computer by performing gestures in sequence. Five different classification models are eval‐ uated (Hidden Markov Models, Support Vector Machines, Cumulative Sum, and two different Neural Network architectures) and Neural Networks are found to be the most effective. The difference in difficulty between classification tasks which either do or do not include observations without intentional movement is also evaluated. The additional requirement to be able to distinguish inten‐ tional gestures from other hand movements is found to increase the difficulty of the task significantly.
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    An extension of the linear regression model for improved vessel trajectory prediction utilising a priori AIS Information
    (Stellenbosch : Stellenbosch University, 2022-04) Burger, Christiaan Neil; Grobler, Trienko Lups; Kleynhans, Waldo; Stellenbosch University. Faculty of Science. Dept. of Computer Science.
    ENGLISH ABSTRACT: As maritime activities increase globally, there is a greater dependency on technology in monitoring, control and surveillance of vessel activity. One of the most prominent systems for monitoring vessel activity is the Automatic Identification System (AIS). An increase in both vessels fitted with AIS transponders, and satellite- and terrestrial receivers has resulted in a significant increase in AIS messages received globally. This resultant rich spatial and temporal data source related to vessel activity provides analysts with the ability to perform enhanced vessel movement analytics, of which a pertinent example is the improvement of vessel location predictions. In this thesis, we propose a novel method for predicting future locations of vessels by making use of historic AIS data. The proposed method extends a Linear Regression Model (LRM), utilising historic AIS movement data in the form of a priori generated spatial maps of the course over ground (LRMAC). The LRMAC has low complexity and is programmatically easy to implement, and attains accurate prediction results. We first compare the LRM with a Discrete Kalman Filter (DKF) on linear trajectories. We then extend the LRM to form the LRMAC. The LRMAC is compared to another method in literature called the Single Point Neighbour Search (SPNS). For the use case of predicting Cargo and Tanker vessel trajectories, with a prediction horizon of up to six hours, the LRMAC has an improved execution time and performance compared to the SPNS.