Cluster analysis for outlier detection : A case study of applying unsupervised machine learning on diesel engine data

With the advent of modern data driven methods, engine manufacturers and maintainers are attempting to pivot from corrective to predictive maintenance. One way to achieve this goal is to install sensors on the engine and look for anomalies in the data patterns it produces. Companies such as Wärtsilä that provide condition monitoring services use the Fast Fourier Transform to manually look for anomalies in the data. The Edge-project is an industrial research project involving institutions such as universities and private companies, with the goal of developing technical solutions and edge analytics for autonomous devices and vessels. Several papers and theses have been written as a result of the project, using techniques such as autoencoders to perform anomaly detection on data produced by sensors on a diesel engine. This thesis explores the use of cluster analysis for anomaly detection on diesel engine data from the Edge-project. Finding clusters could potentially represent different states of the running engine, with anomalies being represented e.g. by data points far away from cluster centroids, or data points not belonging to any particular cluster. The techniques of K-means, DBSCAN and spectral clustering are used for assigning clusters, with silhouette coefficient and eigengap used as hyperparameter tuning heuristics. Distance from cluster centroids and reduced kernel density estimation are used to flag anomalies. T-SNE and Self-Organizing Maps are used as dimensionality reduction techniques to visualize the data into a 3-dimensional and 2-dimensional space, respectively. Results show that what data are flagged as anomalies is highly sensitive to the choice of algorithm and chosen hyperparameters. The different results suggest different data as anomaly candidates. Therefore, further evaluation is needed from subject matter experts to determine which one of the models provides the most interesting results. Further work could include building an ensemble model that combines the used approaches, which could flag certain areas of the data space as a high risk for being anomalous.Moottorien valmistajat ja ylläpitäjät pyrkivät siirtymään korjaavasta huollosta ennakoivaan huoltoon modernien datavetoisten menetelmien avulla. Tämä voidaan saavuttaa esimerkiksi asentamalla antureita moottoriin ja etsimällä poikkeavuuksia anturien tuottamasta datasta. Yritykset kuten Wärtsilä, jotka tarjoavat kunnonvalvontapalveluita etsivät datasta poikkeavuuksia manuaalisesti Fourier-muunnosten avulla. Edge-projekti on teollinen tutkimushanke, johon osallistuu mm. yliopistoja ja yksityisen sektorin yrityksiä, ja jonka tavoitteena on tuottaa teknisiä ratkaisuja ja reunalaskenta-analytiikkaa itseohjautuville laitteille, ajoneuvoille ja aluksille. Hankkeesta on kirjoitettu monia tutkimusartikkeleita ja opinnäytetöitä, joissa käytetään tekniikoita kuten syviä neuroverkkoja poikkeavuuksien havaitsemiseen dieselmoottoriin asennettujen anturien tuottamasta datasta. Tämä opinnäytetyö tutkii klusterianalyysiä menetelmänä poikkeavuuksien havaitsemiseen Edge-projektissa ajetun dieselmoottorin datasta. Klusterit voisivat mahdollisesti edustaa ajettavan moottorin eri tiloja, ja poikkeavuudet voisivat olla esim. kaukana klusterien keskipisteistä olevia datapisteitä, tai datapisteitä, jotka eivät kuulu mihinkään tiettyyn klusteriin. Työssä käytetään algoritmeja K-means, DBSCAN ja spektraaliklusterointia klusterien määrittämiseen, ja siluettikerrointa sekä ominaisväliä käytetään hyperparametrioptimoinnin heuristiikkoina. Poikkeavuuksien merkintään käytetään etäisyyttä klusterien keskipisteisiin sekä alennettua ydintiheysestimaattoria. T-SNE:tä ja itseorganisoituvaa karttaa käytetään datan ulottuvuuksien vähentämisen tekniikoina, jotta data voidaan visualisoida 3- ja 2-ulotteiseen avaruuteen. Tulokset osoittavat, että mikä data tulkitaan poikkeavana, riippuu vahvasti algoritmin ja sen hyperparametrien valinnasta. Menetelmien merkitsemät poikkeavuudet eroavat huomattavasti toisistaan. Tämän vuoksi vaaditaan aihealueen ammattilaisilta lisätutkimuksia, jotta voidaan päättää mikä malli luo mielenkiintoisimmat tulokset. Jatkokehitysideana voisi olla mallikokoelma, jossa yhdistyy tässä työssä käytetyt menetelmät, ja jonka tehtävänä olisi kartoittaa data-avaruuden eri alueiden riskit poikkeavuuksien sisältämiseen

Evaluation of data analytics based clustering algorithms for knowledge mining in a student engagement data

The application of algorithms based on data analytics for the task of knowledge mining in a student dataset is an important strategy for improving learning outcomes, student success and supporting strategic decision making in higher educa�tional institutions of learning. However, the widely used data analytics based clustering algorithms are highly data dependent, making it pertinent to find the most effective algorithm for knowledge mining in a dataset associated with student engage�ment. In this study, performances of five famous clustering algorithms are evaluated for this purpose. The k-means algorithm was benchmarked with 22 distance functions based on the Silhouette index, Dunn’s index and partition entropy internal valid�ity metrics. The hierarchical clustering algorithm was benchmarked with the Cophenetic correlation coefficient computed for different combinations of distance and linkage functions. The Fuzzy c-means algorithm was benchmarked with the partition entropy, partition coefficient, Silhouette index and modified partition coefficient. The k-nearest neighbor algorithm was applied to determine the optimum epsilon value for the density-based spatial clustering of applications with noise. The default param�eter settings were accepted for the expectation-maximization algorithm. The overall ranking of the clustering algorithms was based on cluster potentiality using the median deviation statistics. The results of the evaluation show the well-known k-means algorithm to have the highest cluster potentiality, demonstrating its effectiveness for the task of knowledge mining in a student engagement datase

Usability framework for mobile augmented reality language learning

After several decades since its introduction, the existing ISO9241-11 usability framework is still vastly used in Mobile Augmented Reality (MAR) language learning. The existing framework is generic and can be applied to diverse emerging technologies such as electronic and mobile learning. However, technologies like MAR have interaction properties that are significantly unique and require different usability processes. Hence, implementing the existing framework on MAR can lead to non-optimized, inefficient, and ineffective outcomes. Furthermore, state-of-the-art analysis models such as machine learning are not apparent in MAR usability studies, despite evidence of positive outcomes in other learning technologies. In recent MAR learning studies, machine learning benefits such as problem identification and prioritization were non-existent. These setbacks could slow down the advancement of MAR language learning, which mainly aims to improve language proficiency among MAR users, especially in English communication. Therefore, this research proposed the Usability Framework for MAR (UFMAR) that addressed the currently identified research problems and gaps in language learning. UFMAR introduced an improved data collection method called Individual Interaction Clustering-based Usability Measuring Instrument (IICUMI), followed by a machine learning-driven analysis model called Clustering-based Usability Prioritization Analysis (CUPA) and a prioritization quantifier called Usability Clustering Prioritization Model (UCPM). UFMAR showed empirical evidence of significantly improving usability in MAR, capitalizing on its unique interaction properties. UFMAR enhanced the existing framework with new abilities to systematically identify and prioritize MAR usability issues. Through the experimental results of UFMAR, it was found that the IICUMI method was 50% more effective, while CUPA and UCPM were 57% more effective than the existing framework. The outcome through UFMAR also produced 86% accuracy in analysis results and was 79% more efficient in framework implementation. UFMAR was validated through three cycles of the experimental processes, with triangulation through expert reviews, to be proven as a fitting framework for MAR language learning