Time series anomaly detection can prove to be a very useful tool to inspect and maintain the health and quality of an infrastructure system. While tackling such a problem, the main concern lies in the imbalanced nature of the dataset. In order to mitigate this problem, this thesis proposes two unsupervised anomaly detection frameworks. The first one is an architecture which leverages the concept of matrix profile which essentially refers to a data structure containing the euclidean scores of the subsequences of two time series that is obtained through a similarity join.It is an architecture comprising of a data fusion technique coupled with using matrix profile analysis under the constraints of varied sampling rate for different time series. To this end, we have proposed a framework, through which a time series that is being evaluated for anomalies is quantitatively compared with a benchmark (anomaly-free) time series using the proposed asynchronous time series comparison that was inspired by matrix profile approach for anomaly detection on time series . In order to evaluate the efficacy of this framework, it was tested on a case study comprising of a Class I Rail road dataset. The data collection system integrated into this railway system collects data through different data acquisition channels which represent different transducers. This framework was applied to all the channels and the best performing channels were identified. The average Recall and Precision achieved on the single channel evaluation through this framework was 93.5% and 55% respectively with an error threshold of 0.04 miles or 211 feet. A limitation that was noticed in this framework was that there were some false positive predictions. In order to overcome this problem, a second framework has been proposed which incorporates the idea of extracting signature patterns in a time series also known as motifs which can be leveraged to identify anomalous patterns. This second framework proposed is a motif based framework which operates under the same constraints of a varied sampling rate. Here, a feature extraction method and a clustering method was used in the training process of a One Class Support Vector Machine (OCSVM) coupled with a Kernel Density Estimation (KDE) technique. The average Recall and Precision achieved on the same case study through this frame work was 74% and 57%. In comparison to the first, the second framework does not perform as well. There will be future efforts focused on improving this classification-based anomaly detection method / Master of Science / Time series anomaly detection refers to the identification of any outliers or deviations present in a time series data. This technique could prove to be useful to mitigate any unplanned events by facilitating early maintenance. The first method proposed involves comparing an anomaly-free dataset with the time series of interest. The difference between these two time series are noted and the point with the highest difference will be considered to be an anomaly. The performance of this model was evaluated on a Rail road dataset and the cumuluative average Recall (how useful the predictions are) and average Precison (how accurate the predictions are) 93.5% and 55% respectively with an acceptable error range of 0.04 miles or 211 feet. The second method proposed involves extracting all segments in the anomaly-free dataset and grouping them according to their similarity. Here, a OCSVM is used to train these individual groups. OCSVM is a machine learning algorithm which learns to classify a data as either anomalous or normal. It is then coupled with the KDE which creates a distribution across all the anomalies and identifies the anomaly as one with a high distribution of predictions.The performance of this model was evaluated on a Rail road dataset and the cumulative average Recall and cumulative average Precision 74% and 57% respectively with an acceptable error range of 0.04 miles or 211 feet.

Outlier detection is useful in a vast numbers of different domains, wherever there is data and a need for analysis. The research area related to outlier detection is large and the number of available approaches is constantly growing. Most of the approaches produce a binary result: either outlier or not. In this work approaches that are able to detect outliers by producing a p-value estimate are investigated. Approaches that estimate p-values are interesting since it allows their results to easily be compared against each other, followed over time, or be used with a variable threshold. Four approaches are subjected to a variety of tests to attempt to measure their suitability when the data is distributed in a number of ways. The first approach, the R2S, is developed at Halmstad University. Based on finding the mid-point of the data. The second approach is based on one-class support vector machines (OCSVM). The third and fourth approaches are both based on conformal anomaly detection (CAD), but using different nonconformity measures (NCM). The Mahalanobis distance to the mean and a variation of k-NN are used as NCMs. The R2S and the CAD Mahalanobis are both good at estimating p-values from data generated by unimodal and symmetrical distributions. The CAD k-NN is good at estimating p-values when the data is generated by a bimodal or extremely asymmetric distribution. The OCSVM does not excel in any scenario, but produces good average results in most of the tests. The approaches are also subjected to real data, where they all produce comparable results.

For manufacturing enterprises, the potential of collecting large amounts of data from production processes has enabled the usage of machine learning for prediction-based monitoring and maintenance of machines. Yet common maintenance strategies still include reactive handling of machine failures or schedule-based maintenance conducted by experienced personnel. Both of which are time-consuming and costly for manufacturing enterprises. The incorporation of anomaly detection for production processes alleviates several problems connected to these resource-intensive maintenance strategies. Anomaly detection enables real-time maintenance alarms derived from the occurrence of anomalies and thereby a foundation for proactive maintenance during manufacturing. However, to realize this, one needs to investigate the correlation between machine failure and anomalies in the data. For the machine learning models, it is also of essence to handle the imbalance between failure and normal working condition data. In this work, we investigate the potential of anomaly detection to predict future tool failures of an active CNC-machine based on multivariate time series data collected through the standardized data collection protocol MTConnect. Two semi-supervised anomaly detection methods, DeepAnT and ROCKET OCSVM, were tested. Training and evaluation of the two models were conducted on three production part processes and the difference in anomaly distribution previous to failure and in the normal machine working condition was investigated. The results showed that both models, for all the investigated tool failures belonging to the three production part processes, found an abundance of anomalies preceding failure when compared to the normal working condition of the machines. For certain tool failures, the anomalies were found as far back as seven production cycles before failure, while other anomalies were mainly uncovered close to the failure. Furthermore, it was shown that both models performed optimally with 100 production cycles before tool failures excluded from training, indicating that more anomalies further back connected to failure or possible long-term degradation of machine tools could exist. Lastly, ROCKET OCSVM with RBF kernel showed greater reliability compared to the DeepAnT method in separating the normal working condition data of the CNC machine against the pre-failure data based on anomaly distribution. In conclusion, anomaly detection shows promising results in indicating future machine failure and could serve as a foundation for proactive maintenance strategies of machines. By incorporating proactive strategies, machine downtime, operator maintenance time, and resources and expenses resulting from machine failure could be reduced. / För produktionsföretag har potentialen att samla in stora mängder data från produktionsprocesser möjliggjort användningen av prediktionsbaserad övervakning och underhåll av maskiner genom maskininlärning. Ändå så utgörs fortfarande vanliga underhållsstrategier av reaktiv hantering av maskinfel eller schema baserat underhåll som utförs av erfaren personal. Båda dessa är tidskrävande och kostsamma för tillverkningsföretag. Införandet av anomali detektering för produktionsprocesser lindrar flera problem kopplade till dessa resursintensiva underhållsstrategier. Det möjliggör underhålls-larm i realtid härledda från förekomsten av anomalier, vilket skapar en grund för proaktivt underhåll under tillverkningen. Men för att möjliggöra detta måste man undersöka sambandet mellan maskinfel och anomalier i data utifrån definierade insamlingsmetod. Det är också viktigt att hantera obalansen mellan fel och normal arbetstillstånd data för maskininlärningsmodellerna. I det här arbetet undersöker vi potentialen för delvis övervakad anomali detektering för att förutsäga framtida verktyg fel hos en aktiv CNC-maskin baserat på multivariat tidsseriedata som samlats in genom det standardiserade datainsamling protokollet MT Connect. Två anomali detekterings metoder som endast tränats på normala arbetsförhållanden för maskiner testades, DeepAnT och ROCKET OCSVM. Träning och utvärdering av de två modellerna genomfördes på tre produktionsdelprocesser och skillnaden i anomali fördelning före fel och i det normala maskinens arbetstillstånd undersöktes. Resultaten visade att båda modellerna, för alla undersökta verktygsfel som hör till de tre produktionsdelprocesserna, fann ett överflöd av anomalier före fel i jämförelse med maskinernas normala arbetstillstånd. För vissa verktygsfel hittades anomalierna så långt tillbaka som sju produktionscykler före fel, medan andra anomalier huvudsakligen upptäcktes nära felet. Vidare visades det att båda modellerna presterar optimalt med 100 produktionscykler före verktygsfel uteslutna från träningen, vilket tyder på att fler anomalier tidigare än de åtta produktions cyklarna undersökta innan fel eller eventuell långvarig försämring av verktygsmaskiner kan förekomma. Slutligen visade ROCKET OCSVM med RBF som kärnfunktion större tillförlitlighet i jämförelse med DeepAnT metoden gällande att separera CNC-maskinens normala arbetstillstånd data från pre-failure-data baserat på anomali fördelning. Sammanfattningsvis visar avvikelse detektering lovande resultat för att indikera framtida maskinfel och kan fungera som en grund för proaktivt underhåll av maskiner. Genom att införskaffa proaktiva strategier kan maskinernas stilleståndstid, operatörens underhållstid samt resurser och kostnader till följd av maskinfel minskas.